------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\mbeissin\Desktop\Stata files for book\Logfiles\chapter4.log
  log type:  text
 opened on:  25 Jan 2022, 22:03:27

. * ============================================================================
. * STATISTICAL RESULTS APPEARING IN CHAPTER 4
. * STATA Do file for Chapter 4  
. * Results reported in Chapter 4  
. * Author: Mark R. Beissinger  
. * Date:  January 2022  
. * Princeton, NJ 
. * =============================================================================
. * BEFORE RUNNING, YOU MUST SET THE DEFAULT PATH FOR WHERE THE DATA
. *   FILES RESIDE
. * =============================================================================
. * Before running, you must download the following packages for STATA:
. *   looclass from http://fmwww.bc.edu/RePEc/bocode/l
. *   mimrgns from http://fmwww.bc.edu/RePEc/bocode/m
. * =============================================================================
. * The following datafiles are used in this chapter:
. *   States and episodes--statesandepisodes.dta 
. *   Dataset of revolutionary episodes--revolutionaryeps.dta
. *   Dataset of revolutionary episodes, multiple imputation (regime model)--revolutionaryepsmireg.dta
. *       Dataset of revolutionary episodes, multiple imputation (opposition model)--revolutionaryepsmiopp.dta
. *       Dataset of revolutionary episodes, multiple imputation (combined model)--revolutionaryepsmicomb.dta
. * =============================================================================
. * Output produced:  Logfiles\chapter4.log
. *                                       Logfiles\figure4_6a.pdf
. *                                       Logfiles\figure4_6b.pdf
. *                                       Logfiles\figure4_10a.pdf
. *                                       Logfiles\figure4_10b.pdf
. *       --All output from this chapter has been combined into a single file and can 
. *               be found in a pdf file (chapter4.pdf) in the "Outputfiles" folder
. * =============================================================================
. 
. 
. * ====================
. * DATA FOR FIGURE 4.1
. * ====================
. use revolutionaryeps.dta

. tab fiveyrperiodstr success if startyear>1899

 Five-year | Succeeded in gaining
    period |        power?
    string |        no        yes |     Total
-----------+----------------------+----------
   1900-04 |         8          0 |         8 
   1905-09 |         8          2 |        10 
   1910-14 |         3          3 |         6 
   1915-19 |        17         10 |        27 
   1920-24 |        16          3 |        19 
   1925-29 |        10          1 |        11 
   1930-34 |         8          2 |        10 
   1935-39 |         6          1 |         7 
   1940-44 |         4          4 |         8 
   1945-49 |        11          5 |        16 
   1950-54 |         4          5 |         9 
   1955-59 |         9          3 |        12 
   1960-64 |        11         10 |        21 
   1965-69 |        12          5 |        17 
   1970-74 |         8          4 |        12 
   1975-79 |         8          5 |        13 
   1980-84 |        10          4 |        14 
   1985-89 |        11         15 |        26 
   1990-94 |        14         16 |        30 
   1995-99 |         5          4 |         9 
   2000-04 |         7          6 |        13 
   2005-09 |        15          4 |        19 
   2010-14 |        15         11 |        26 
-----------+----------------------+----------
     Total |       220        123 |       343 


. 
. * ======================================================================
. * COUNT MODELS, EFFECT OF TIME ON NUMBER OF SUCCESSFUL REVOLUITIONS AND 
. *   NUMBER OF FAILED REVOLUTIONS (WITH AND W/OUT COLLAPSE OF COMMUNISM
. * ======================================================================
. clear

. use statesandepisodes.dta

. * Failed cases
. poisson failed year , vce(robust) irr nolog

Poisson regression                              Number of obs     =        115
                                                Wald chi2(1)      =       1.11
                                                Prob > chi2       =     0.2917
Log pseudolikelihood = -197.45223               Pseudo R2         =     0.0033

------------------------------------------------------------------------------
             |               Robust
      failed |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        year |   1.002336   .0022179     1.05   0.292     .9979983    1.006693
       _cons |    .019831   .0860873    -0.90   0.366     4.00e-06    98.27711
------------------------------------------------------------------------------

. estat gof

         Deviance goodness-of-fit =  145.5044
         Prob > chi2(113)         =    0.0213

         Pearson goodness-of-fit  =  127.3602
         Prob > chi2(113)         =    0.1682

. *  Result:  Failed goodness of fit test--must use negative binomial
. nbreg failed year , vce(robust) irr nolog

Negative binomial regression                    Number of obs     =        115
                                                Wald chi2(1)      =       1.11
Dispersion           = mean                     Prob > chi2       =     0.2910
Log pseudolikelihood = -197.16558               Pseudo R2         =     0.0030

------------------------------------------------------------------------------
             |               Robust
      failed |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        year |   1.002318   .0021981     1.06   0.291     .9980192    1.006636
       _cons |   .0205336   .0883428    -0.90   0.366     4.47e-06    94.32778
-------------+----------------------------------------------------------------
    /lnalpha |  -2.890079   1.327496                     -5.491923   -.2882358
-------------+----------------------------------------------------------------
       alpha |   .0555718   .0737713                      .0041199    .7495849
------------------------------------------------------------------------------

. * Successful cases
. poisson success year , vce(robust) irr nolog

Poisson regression                              Number of obs     =        115
                                                Wald chi2(1)      =      15.21
                                                Prob > chi2       =     0.0001
Log pseudolikelihood = -159.56227               Pseudo R2         =     0.0598

------------------------------------------------------------------------------
             |               Robust
     success |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        year |   1.012631    .003259     3.90   0.000     1.006263    1.019038
       _cons |   2.11e-11   1.34e-10    -3.87   0.000     8.23e-17    5.41e-06
------------------------------------------------------------------------------

. estat gof

         Deviance goodness-of-fit =  157.7972
         Prob > chi2(113)         =    0.0035

         Pearson goodness-of-fit  =  172.2202
         Prob > chi2(113)         =    0.0003

. *  Result:  Failed goodness of fit test--must use negative binomial
. nbreg success year , vce(robust) irr nolog

Negative binomial regression                    Number of obs     =        115
                                                Wald chi2(1)      =      14.30
Dispersion           = mean                     Prob > chi2       =     0.0002
Log pseudolikelihood = -156.14208               Pseudo R2         =     0.0434

------------------------------------------------------------------------------
             |               Robust
     success |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        year |   1.012881   .0034286     3.78   0.000     1.006184    1.019624
       _cons |   1.30e-11   8.67e-11    -3.75   0.000     2.68e-17    6.28e-06
-------------+----------------------------------------------------------------
    /lnalpha |  -1.065712    .447845                     -1.943472   -.1879516
-------------+----------------------------------------------------------------
       alpha |   .3444826   .1542748                      .1432059    .8286548
------------------------------------------------------------------------------

. * Without collapse of European communism cases
. nbreg failnocommcoll year , vce(robust) irr nolog

Negative binomial regression                    Number of obs     =        115
                                                Wald chi2(1)      =       0.45
Dispersion           = mean                     Prob > chi2       =     0.5034
Log pseudolikelihood = -195.29915               Pseudo R2         =     0.0012

--------------------------------------------------------------------------------
               |               Robust
failnocommcoll |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
          year |   1.001514   .0022645     0.67   0.503     .9970856    1.005962
         _cons |   .0953175   .4224837    -0.53   0.596     .0000161    564.9695
---------------+----------------------------------------------------------------
      /lnalpha |  -2.792818   1.300848                     -5.342432   -.2432028
---------------+----------------------------------------------------------------
         alpha |   .0612484   .0796748                      .0047842    .7841124
--------------------------------------------------------------------------------

. nbreg succnocommcoll year , vce(robust) irr nolog

Negative binomial regression                    Number of obs     =        115
                                                Wald chi2(1)      =      11.17
Dispersion           = mean                     Prob > chi2       =     0.0008
Log pseudolikelihood = -147.37464               Pseudo R2         =     0.0358

--------------------------------------------------------------------------------
               |               Robust
succnocommcoll |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
          year |   1.010638   .0031998     3.34   0.001     1.004386    1.016929
         _cons |   9.22e-10   5.78e-09    -3.32   0.001     4.28e-15    .0001989
---------------+----------------------------------------------------------------
      /lnalpha |   -1.91692   1.020059                     -3.916198    .0823584
---------------+----------------------------------------------------------------
         alpha |   .1470593   .1500091                      .0199167    1.085845
--------------------------------------------------------------------------------

. 
. * ===========================
. * PREDICTIONS OF COUNT MODEL
. * ===========================
. nbreg success year , vce(robust) irr nolog

Negative binomial regression                    Number of obs     =        115
                                                Wald chi2(1)      =      14.30
Dispersion           = mean                     Prob > chi2       =     0.0002
Log pseudolikelihood = -156.14208               Pseudo R2         =     0.0434

------------------------------------------------------------------------------
             |               Robust
     success |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        year |   1.012881   .0034286     3.78   0.000     1.006184    1.019624
       _cons |   1.30e-11   8.67e-11    -3.75   0.000     2.68e-17    6.28e-06
-------------+----------------------------------------------------------------
    /lnalpha |  -1.065712    .447845                     -1.943472   -.1879516
-------------+----------------------------------------------------------------
       alpha |   .3444826   .1542748                      .1432059    .8286548
------------------------------------------------------------------------------

. predict succpred
(option n assumed; predicted number of events)

. list year succpred

     +-----------------+
     | year   succpred |
     |-----------------|
  1. | 1900   .4726209 |
  2. | 1901    .478709 |
  3. | 1902   .4848755 |
  4. | 1903   .4911214 |
  5. | 1904   .4974478 |
     |-----------------|
  6. | 1905   .5038556 |
  7. | 1906    .510346 |
  8. | 1907     .51692 |
  9. | 1908   .5235787 |
 10. | 1909   .5303232 |
     |-----------------|
 11. | 1910   .5371546 |
 12. | 1911   .5440739 |
 13. | 1912   .5510824 |
 14. | 1913   .5581811 |
 15. | 1914   .5653713 |
     |-----------------|
 16. | 1915   .5726541 |
 17. | 1916   .5800307 |
 18. | 1917   .5875024 |
 19. | 1918   .5950703 |
 20. | 1919   .6027357 |
     |-----------------|
 21. | 1920   .6104998 |
 22. | 1921   .6183639 |
 23. | 1922   .6263294 |
 24. | 1923   .6343974 |
 25. | 1924   .6425694 |
     |-----------------|
 26. | 1925   .6508467 |
 27. | 1926   .6592305 |
 28. | 1927   .6677223 |
 29. | 1928   .6763236 |
 30. | 1929   .6850356 |
     |-----------------|
 31. | 1930   .6938599 |
 32. | 1931   .7027979 |
 33. | 1932   .7118509 |
 34. | 1933   .7210206 |
 35. | 1934   .7303084 |
     |-----------------|
 36. | 1935   .7397159 |
 37. | 1936   .7492445 |
 38. | 1937   .7588959 |
 39. | 1938   .7686716 |
 40. | 1939   .7785732 |
     |-----------------|
 41. | 1940   .7886024 |
 42. | 1941   .7987607 |
 43. | 1942     .80905 |
 44. | 1943   .8194717 |
 45. | 1944   .8300277 |
     |-----------------|
 46. | 1945   .8407197 |
 47. | 1946   .8515494 |
 48. | 1947   .8625186 |
 49. | 1948   .8736292 |
 50. | 1949   .8848827 |
     |-----------------|
 51. | 1950   .8962814 |
 52. | 1951   .9078268 |
 53. | 1952   .9195209 |
 54. | 1953   .9313657 |
 55. | 1954   .9433631 |
     |-----------------|
 56. | 1955    .955515 |
 57. | 1956   .9678234 |
 58. | 1957   .9802904 |
 59. | 1958    .992918 |
 60. | 1959   1.005708 |
     |-----------------|
 61. | 1960   1.018663 |
 62. | 1961   1.031785 |
 63. | 1962   1.045076 |
 64. | 1963   1.058538 |
 65. | 1964   1.072174 |
     |-----------------|
 66. | 1965   1.085985 |
 67. | 1966   1.099974 |
 68. | 1967   1.114143 |
 69. | 1968   1.128495 |
 70. | 1969   1.143032 |
     |-----------------|
 71. | 1970   1.157756 |
 72. | 1971   1.172669 |
 73. | 1972   1.187775 |
 74. | 1973   1.203075 |
 75. | 1974   1.218573 |
     |-----------------|
 76. | 1975    1.23427 |
 77. | 1976   1.250169 |
 78. | 1977   1.266273 |
 79. | 1978   1.282584 |
 80. | 1979   1.299106 |
     |-----------------|
 81. | 1980    1.31584 |
 82. | 1981    1.33279 |
 83. | 1982   1.349959 |
 84. | 1983   1.367348 |
 85. | 1984   1.384961 |
     |-----------------|
 86. | 1985   1.402802 |
 87. | 1986   1.420872 |
 88. | 1987   1.439175 |
 89. | 1988   1.457714 |
 90. | 1989   1.476491 |
     |-----------------|
 91. | 1990   1.495511 |
 92. | 1991   1.514775 |
 93. | 1992   1.534287 |
 94. | 1993   1.554051 |
 95. | 1994    1.57407 |
     |-----------------|
 96. | 1995   1.594346 |
 97. | 1996   1.614884 |
 98. | 1997   1.635686 |
 99. | 1998   1.656756 |
100. | 1999   1.678097 |
     |-----------------|
101. | 2000   1.699714 |
102. | 2001   1.721608 |
103. | 2002   1.743785 |
104. | 2003   1.766248 |
105. | 2004      1.789 |
     |-----------------|
106. | 2005   1.812045 |
107. | 2006   1.835387 |
108. | 2007   1.859029 |
109. | 2008   1.882976 |
110. | 2009   1.907231 |
     |-----------------|
111. | 2010   1.931799 |
112. | 2011   1.956684 |
113. | 2012   1.981889 |
114. | 2013   2.007418 |
115. | 2014   2.033277 |
     +-----------------+

. 
. * ====================
. * DATA FOR FIGURE 4.2
. * ====================
. clear

. use revolutionaryeps.dta

. logit success startyear if startyear>1899, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(1)        =       8.36
                                                Prob > chi2       =     0.0038
Log likelihood = -219.66306                     Pseudo R2         =     0.0187

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   startyear |   1.009978   .0035236     2.85   0.004     1.003095    1.016908
       _cons |   1.88e-09   1.29e-08    -2.93   0.003     2.71e-15    .0013053
------------------------------------------------------------------------------

. margins, at(startyear=(1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985
>  1990 1995 2000 2005 2010 2015))

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()

1._at        : startyear       =        1900

2._at        : startyear       =        1905

3._at        : startyear       =        1910

4._at        : startyear       =        1915

5._at        : startyear       =        1920

6._at        : startyear       =        1925

7._at        : startyear       =        1930

8._at        : startyear       =        1935

9._at        : startyear       =        1940

10._at       : startyear       =        1945

11._at       : startyear       =        1950

12._at       : startyear       =        1955

13._at       : startyear       =        1960

14._at       : startyear       =        1965

15._at       : startyear       =        1970

16._at       : startyear       =        1975

17._at       : startyear       =        1980

18._at       : startyear       =        1985

19._at       : startyear       =        1990

20._at       : startyear       =        1995

21._at       : startyear       =        2000

22._at       : startyear       =        2005

23._at       : startyear       =        2010

24._at       : startyear       =        2015

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .226646   .0454169     4.99   0.000     .1376304    .3156615
          2  |    .235465   .0438476     5.37   0.000     .1495254    .3214047
          3  |   .2445187   .0421733     5.80   0.000     .1618605    .3271769
          4  |    .253805   .0404079     6.28   0.000     .1746069    .3330031
          5  |    .263321     .03857     6.83   0.000     .1877251    .3389168
          6  |   .2730632   .0366844     7.44   0.000     .2011631    .3449633
          7  |   .2830274   .0347838     8.14   0.000     .2148525    .3512023
          8  |   .2932085   .0329108     8.91   0.000     .2287045    .3577126
          9  |   .3036008   .0311205     9.76   0.000     .2426058    .3645958
         10  |   .3141977   .0294818    10.66   0.000     .2564145    .3719809
         11  |   .3249918   .0280786    11.57   0.000     .2699587     .380025
         12  |   .3359751   .0270069    12.44   0.000     .2830426    .3889077
         13  |   .3471387    .026366    13.17   0.000     .2954623    .3988152
         14  |    .358473    .026244    13.66   0.000     .3070357    .4099104
         15  |   .3699677   .0266994    13.86   0.000     .3176378    .4222976
         16  |   .3816117   .0277484    13.75   0.000     .3272258    .4359975
         17  |   .3933933   .0293637    13.40   0.000     .3358416     .450945
         18  |   .4053002   .0314858    12.87   0.000     .3435892    .4670113
         19  |   .4173197   .0340392    12.26   0.000      .350604    .4840353
         20  |   .4294381   .0369454    11.62   0.000     .3570265    .5018497
         21  |   .4416418   .0401312    11.00   0.000     .3629862    .5202974
         22  |   .4539163    .043532    10.43   0.000     .3685952    .5392374
         23  |   .4662472   .0470922     9.90   0.000     .3739482    .5585461
         24  |   .4786194    .050764     9.43   0.000     .3791237    .5781151
------------------------------------------------------------------------------

. 
. * ====================
. * DATA FOR FIGURE 4.3
. * ====================
. logit success startyear urbandum if startprior1900==0, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(2)        =      21.17
                                                Prob > chi2       =     0.0000
Log likelihood = -213.25837                     Pseudo R2         =     0.0473

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   startyear |   1.008273   .0035918     2.31   0.021     1.001257    1.015337
    urbandum |    2.31381   .5500442     3.53   0.000     1.452038    3.687035
       _cons |   3.26e-08   2.28e-07    -2.47   0.014     3.64e-14     .029195
------------------------------------------------------------------------------

. margins, atmeans at(urbandum=(0 1))

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()

1._at        : startyear       =    1963.443 (mean)
               urbandum        =           0

2._at        : startyear       =    1963.443 (mean)
               urbandum        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2568209   .0346933     7.40   0.000     .1888233    .3248186
          2  |   .4443163   .0376727    11.79   0.000     .3704791    .5181535
------------------------------------------------------------------------------

. logit success startyear urbancivic if startprior1900==0, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(2)        =      18.82
                                                Prob > chi2       =     0.0001
Log likelihood = -214.43637                     Pseudo R2         =     0.0420

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   startyear |   1.005104   .0038348     1.33   0.182     .9976164    1.012649
  urbancivic |    2.90011   .9682882     3.19   0.001     1.507354    5.579734
       _cons |   .0000211   .0001578    -1.44   0.150     9.10e-12    48.93537
------------------------------------------------------------------------------

. margins, atmeans at(urbancivic=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : startyear       =    1963.443 (mean)
               urbancivic      =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5733315   .0740802     7.74   0.000     .4281371     .718526
------------------------------------------------------------------------------

. logit success startyear urbanleftist if startprior1900==0, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(2)        =      11.02
                                                Prob > chi2       =     0.0040
Log likelihood = -218.33278                     Pseudo R2         =     0.0246

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   startyear |   1.008987   .0035597     2.54   0.011     1.002035    1.015989
urbanleftist |   .4792472   .2284422    -1.54   0.123     .1882861    1.219834
       _cons |   1.37e-08   9.50e-08    -2.61   0.009     1.68e-14    .0111439
------------------------------------------------------------------------------

. margins, atmeans at(urbanleftist=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : startyear       =    1963.443 (mean)
               urbanleftist    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2184294   .0784699     2.78   0.005     .0646311    .3722276
------------------------------------------------------------------------------

. logit success startyear ruralleftist if startprior1900==0, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(2)        =       9.23
                                                Prob > chi2       =     0.0099
Log likelihood = -219.23057                     Pseudo R2         =     0.0206

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   startyear |   1.009787   .0035139     2.80   0.005     1.002924    1.016698
ruralleftist |   .7167856   .2609578    -0.91   0.360     .3511519    1.463132
       _cons |   2.83e-09   1.94e-08    -2.87   0.004     4.21e-15    .0019068
------------------------------------------------------------------------------

. margins, atmeans at(ruralleftist=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : startyear       =    1963.443 (mean)
               ruralleftist    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2909317   .0707329     4.11   0.000     .1522976    .4295657
------------------------------------------------------------------------------

. logit success startyear otherrev if startprior1900==0, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(2)        =      10.84
                                                Prob > chi2       =     0.0044
Log likelihood = -218.42615                     Pseudo R2         =     0.0242

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   startyear |   1.009134   .0035773     2.57   0.010     1.002147     1.01617
    otherrev |   .6902005   .1624062    -1.58   0.115     .4351962    1.094625
       _cons |   1.22e-08   8.52e-08    -2.61   0.009     1.36e-14    .0109357
------------------------------------------------------------------------------

. margins, atmeans at(otherrev=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : startyear       =    1963.443 (mean)
               otherrev        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3227028   .0324324     9.95   0.000     .2591366    .3862691
------------------------------------------------------------------------------

. * Excluding cases associated with collapse of European communism
. logit success startyear urbancivic  if startyear>1899 & incgovcommunist==0 , or nolog

Logistic regression                             Number of obs     =        313
                                                LR chi2(2)        =      11.24
                                                Prob > chi2       =     0.0036
Log likelihood = -197.30859                     Pseudo R2         =     0.0277

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   startyear |   1.004215   .0039175     1.08   0.281      .996566    1.011923
  urbancivic |   2.430963     .87861     2.46   0.014     1.197107     4.93655
       _cons |   .0001226   .0009366    -1.18   0.239     3.83e-11    391.8203
------------------------------------------------------------------------------

. * Restricting sample to post-Cold War cases
. logit success urbancivic  if  startyear>1984, or nolog

Logistic regression                             Number of obs     =        123
                                                LR chi2(1)        =       6.07
                                                Prob > chi2       =     0.0137
Log likelihood = -81.727229                     Pseudo R2         =     0.0358

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  urbancivic |   2.526316    .961639     2.43   0.015     1.198061    5.327169
       _cons |   .5833333   .1387152    -2.27   0.023     .3660187    .9296733
------------------------------------------------------------------------------

. 
. 
. * ==================================================
. * Regime characteristics and revolutionary outcomes
. * ==================================================
. * ===========================================================
. * DATA FOR FIGURE 4.4 on effect of Polity scores on outcomes
. * ===========================================================
. * Testing for quadratic specification on complete sample
. logit success politymin1 if startyear>1899 , or nolog

Logistic regression                             Number of obs     =        272
                                                LR chi2(1)        =      17.26
                                                Prob > chi2       =     0.0000
Log likelihood = -173.23362                     Pseudo R2         =     0.0475

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  politymin1 |   .9121584   .0209893    -4.00   0.000      .871934    .9542385
       _cons |   .5373615   .0745656    -4.48   0.000     .4094036    .7053122
------------------------------------------------------------------------------

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        272 -181.8636  -173.2336       2    350.4672   357.6788
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. logit success politymin1 politymin1sq if startyear>1899 , or nolog

Logistic regression                             Number of obs     =        272
                                                LR chi2(2)        =      23.52
                                                Prob > chi2       =     0.0000
Log likelihood = -170.10178                     Pseudo R2         =     0.0647

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  politymin1 |    .890595    .024434    -4.22   0.000     .8439701    .9397956
politymin1sq |   .9874349   .0051461    -2.43   0.015     .9774002    .9975727
       _cons |    .792373   .1642329    -1.12   0.262     .5278415    1.189476
------------------------------------------------------------------------------

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        272 -181.8636  -170.1018       3    346.2036    357.021
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Use Multiple imputation sample for all episodes, excluding colonies
. clear

. use revolutionaryepsmireg.dta

. set seed 1234

. * Outcomes for all episodes
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq if startyear>18
> 99

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0339
                                                Largest FMI       =     0.0718
DF adjustment:   Large sample                   DF:     min       =   3,738.70
                                                        avg       =  10,760.55
                                                        max       =  14,426.71
Model F test:       Equal FMI                   F(   2,13437.8)   =       9.09
Within VCE type:          OIM                   Prob > F          =     0.0001

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |   .8950604   .0238367    -4.16   0.000     .8495359    .9430243
newpolitymin1sq |   .9868593   .0050673    -2.58   0.010     .9769742    .9968444
          _cons |   .7930292   .1640453    -1.12   0.262     .5286829    1.189551
---------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=-10 newpolitymin1sq=100) at(newpolitymin1=-9 newpolitymin1sq=81) at(newpolitymin1=-8 n
> ewpolitymin1sq=64) at(newpolitymin1=-7 newpolitymin1sq=49) at(newpolitymin1=-6 newpolitymin1sq=36) at(newpolitym
> in1=-5 newpolitymin1sq=25) at(newpolitymin1=-4 newpolitymin1sq=16) at(newpolitymin1=-3 newpolitymin1sq=9) at(new
> politymin1=-2 newpolitymin1sq=4) at(newpolitymin1=-1 newpolitymin1sq=1) at(newpolitymin1=0 newpolitymin1sq=0) at
> (newpolitymin1=1 newpolitymin1sq=1) at(newpolitymin1=2 newpolitymin1sq=4) at(newpolitymin1=3 newpolitymin1sq=9) 
> at(newpolitymin1=4 newpolitymin1sq=16) at(newpolitymin1=5 newpolitymin1sq=25) at(newpolitymin1=6 newpolitymin1sq
> =36) at(newpolitymin1=7 newpolitymin1sq=49) at(newpolitymin1=8 newpolitymin1sq=64) at(newpolitymin1=9 newpolitym
> in1sq=81) at(newpolitymin1=10 newpolitymin1sq=100) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0129
                                                Largest FMI       =     0.0602
DF adjustment:   Large sample                   DF:     min       =   5,309.96
                                                        avg       =  37,481.33
Within VCE type: Delta-method                           max       = 150,325.39

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =         -10
               newpolitym~q    =         100

2._at        : newpolitym~1    =          -9
               newpolitym~q    =          81

3._at        : newpolitym~1    =          -8
               newpolitym~q    =          64

4._at        : newpolitym~1    =          -7
               newpolitym~q    =          49

5._at        : newpolitym~1    =          -6
               newpolitym~q    =          36

6._at        : newpolitym~1    =          -5
               newpolitym~q    =          25

7._at        : newpolitym~1    =          -4
               newpolitym~q    =          16

8._at        : newpolitym~1    =          -3
               newpolitym~q    =           9

9._at        : newpolitym~1    =          -2
               newpolitym~q    =           4

10._at       : newpolitym~1    =          -1
               newpolitym~q    =           1

11._at       : newpolitym~1    =           0
               newpolitym~q    =           0

12._at       : newpolitym~1    =           1
               newpolitym~q    =           1

13._at       : newpolitym~1    =           2
               newpolitym~q    =           4

14._at       : newpolitym~1    =           3
               newpolitym~q    =           9

15._at       : newpolitym~1    =           4
               newpolitym~q    =          16

16._at       : newpolitym~1    =           5
               newpolitym~q    =          25

17._at       : newpolitym~1    =           6
               newpolitym~q    =          36

18._at       : newpolitym~1    =           7
               newpolitym~q    =          49

19._at       : newpolitym~1    =           8
               newpolitym~q    =          64

20._at       : newpolitym~1    =           9
               newpolitym~q    =          81

21._at       : newpolitym~1    =          10
               newpolitym~q    =         100

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3904147   .0793062     4.92   0.000     .2349589    .5458704
          2  |    .424233   .0632846     6.70   0.000     .3001885    .5482775
          3  |   .4522643   .0501273     9.02   0.000     .3540138    .5505148
          4  |   .4740228   .0416244    11.39   0.000     .3924399    .5556057
          5  |   .4892743   .0386025    12.67   0.000     .4136141    .5649345
          6  |   .4979327   .0399237    12.47   0.000     .4196811    .5761842
          7  |   .4999789   .0432971    11.55   0.000     .4151122    .5848456
          8  |   .4954131   .0468846    10.57   0.000     .4035122     .587314
          9  |   .4842413   .0496516     9.75   0.000     .3869156    .5815671
         10  |   .4664983   .0510891     9.13   0.000     .3663553    .5666413
         11  |   .4423048   .0510086     8.67   0.000     .3423212    .5422885
         12  |   .4119563   .0495026     8.32   0.000     .3149272    .5089855
         13  |   .3760311   .0470007     8.00   0.000     .2839081    .4681541
         14  |   .3354935   .0443083     7.57   0.000     .2486494    .4223377
         15  |   .2917521   .0424162     6.88   0.000     .2086168    .3748874
         16  |   .2466243   .0419249     5.88   0.000     .1644513    .3287974
         17  |    .202177   .0424705     4.76   0.000     .1189319     .285422
         18  |   .1604535   .0429139     3.74   0.000     .0763353    .2445717
         19  |   .1231606   .0421297     2.92   0.003     .0405752    .2057459
         20  |   .0914176   .0395774     2.31   0.021      .013832    .1690032
         21  |   .0656533   .0353785     1.86   0.064    -.0037031    .1350097
------------------------------------------------------------------------------

. * Outcomes for all rural episodes
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq  if startyear>1
> 899 & urbandum==0

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        123
                                                Average RVI       =     0.0211
                                                Largest FMI       =     0.0433
DF adjustment:   Large sample                   DF:     min       =  10,223.05
                                                        avg       =  62,876.17
                                                        max       = 152,065.03
Model F test:       Equal FMI                   F(   2,35652.5)   =       4.21
Within VCE type:          OIM                   Prob > F          =     0.0149

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |    .866733   .0428024    -2.90   0.004     .7867733    .9548189
newpolitymin1sq |   .9923637   .0088134    -0.86   0.388     .9752372    1.009791
          _cons |   .3169799   .1147549    -3.17   0.002     .1559052    .6444701
---------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=-10 newpolitymin1sq=100) at(newpolitymin1=-9 newpolitymin1sq=81) at(newpolitymin1=-8 n
> ewpolitymin1sq=64) at(newpolitymin1=-7 newpolitymin1sq=49) at(newpolitymin1=-6 newpolitymin1sq=36) at(newpolitym
> in1=-5 newpolitymin1sq=25) at(newpolitymin1=-4 newpolitymin1sq=16) at(newpolitymin1=-3 newpolitymin1sq=9) at(new
> politymin1=-2 newpolitymin1sq=4) at(newpolitymin1=-1 newpolitymin1sq=1) at(newpolitymin1=0 newpolitymin1sq=0) at
> (newpolitymin1=1 newpolitymin1sq=1) at(newpolitymin1=2 newpolitymin1sq=4) at(newpolitymin1=3 newpolitymin1sq=9) 
> at(newpolitymin1=4 newpolitymin1sq=16) at(newpolitymin1=5 newpolitymin1sq=25) at(newpolitymin1=6 newpolitymin1sq
> =36) at(newpolitymin1=7 newpolitymin1sq=49) at(newpolitymin1=8 newpolitymin1sq=64) at(newpolitymin1=9 newpolitym
> in1sq=81) at(newpolitymin1=10 newpolitymin1sq=100) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        123
                                                Average RVI       =     0.0096
                                                Largest FMI       =     0.0414
DF adjustment:   Large sample                   DF:     min       =  11,163.33
                                                        avg       = 106,207.96
Within VCE type: Delta-method                           max       = 463,903.69

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =         -10
               newpolitym~q    =         100

2._at        : newpolitym~1    =          -9
               newpolitym~q    =          81

3._at        : newpolitym~1    =          -8
               newpolitym~q    =          64

4._at        : newpolitym~1    =          -7
               newpolitym~q    =          49

5._at        : newpolitym~1    =          -6
               newpolitym~q    =          36

6._at        : newpolitym~1    =          -5
               newpolitym~q    =          25

7._at        : newpolitym~1    =          -4
               newpolitym~q    =          16

8._at        : newpolitym~1    =          -3
               newpolitym~q    =           9

9._at        : newpolitym~1    =          -2
               newpolitym~q    =           4

10._at       : newpolitym~1    =          -1
               newpolitym~q    =           1

11._at       : newpolitym~1    =           0
               newpolitym~q    =           0

12._at       : newpolitym~1    =           1
               newpolitym~q    =           1

13._at       : newpolitym~1    =           2
               newpolitym~q    =           4

14._at       : newpolitym~1    =           3
               newpolitym~q    =           9

15._at       : newpolitym~1    =           4
               newpolitym~q    =          16

16._at       : newpolitym~1    =           5
               newpolitym~q    =          25

17._at       : newpolitym~1    =           6
               newpolitym~q    =          36

18._at       : newpolitym~1    =           7
               newpolitym~q    =          49

19._at       : newpolitym~1    =           8
               newpolitym~q    =          64

20._at       : newpolitym~1    =           9
               newpolitym~q    =          81

21._at       : newpolitym~1    =          10
               newpolitym~q    =         100

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3813316   .1330983     2.87   0.004     .1204355    .6422278
          2  |   .3817987   .1031027     3.70   0.000     .1797052    .5838922
          3  |   .3787138   .0797977     4.75   0.000      .222307    .5351206
          4  |   .3720929   .0650555     5.72   0.000     .2445852    .4996005
          5  |   .3620031   .0595982     6.07   0.000     .2451926    .4788137
          6  |   .3485684   .0610181     5.71   0.000     .2289744    .4681623
          7  |   .3319787   .0651376     5.10   0.000      .204309    .4596483
          8  |   .3125028   .0687977     4.54   0.000     .1776576    .4473481
          9  |   .2904998   .0704101     4.13   0.000     .1524928    .4285068
         10  |   .2664268   .0694645     3.84   0.000     .1302725     .402581
         11  |    .240838   .0661419     3.64   0.000     .1111965    .3704795
         12  |   .2143719   .0611132     3.51   0.000     .0945876    .3341561
         13  |   .1877232   .0553908     3.39   0.001     .0791564      .29629
         14  |   .1616008   .0501121     3.22   0.001     .0633814    .2598202
         15  |    .136676   .0461792     2.96   0.003      .046166    .2271861
         16  |   .1135298   .0438508     2.59   0.010     .0275835    .1994762
         17  |   .0926079   .0426456     2.17   0.030     .0090236    .1761922
         18  |   .0741934   .0417248     1.78   0.075    -.0075864    .1559732
         19  |   .0584007   .0403809     1.45   0.148    -.0207459    .1375472
         20  |   .0451904   .0382631     1.18   0.238     -.029806    .1201868
         21  |   .0343999   .0353512     0.97   0.331    -.0348901    .1036898
------------------------------------------------------------------------------

. * Outcomes for all urban episodes
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq  if startyear>1
> 899 & urbandum==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        165
                                                Average RVI       =     0.0373
                                                Largest FMI       =     0.0803
DF adjustment:   Large sample                   DF:     min       =   2,991.85
                                                        avg       =   7,913.43
                                                        max       =  12,285.05
Model F test:       Equal FMI                   F(   2,11240.3)   =       5.27
Within VCE type:          OIM                   Prob > F          =     0.0052

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |   .9096843   .0304155    -2.83   0.005     .8519743    .9713034
newpolitymin1sq |   .9828808   .0065665    -2.58   0.010     .9700894    .9958408
          _cons |   1.427177   .3876102     1.31   0.190     .8380605    2.430415
---------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=-10 newpolitymin1sq=100) at(newpolitymin1=-9 newpolitymin1sq=81) at(newpolitymin1=-8 n
> ewpolitymin1sq=64) at(newpolitymin1=-7 newpolitymin1sq=49) at(newpolitymin1=-6 newpolitymin1sq=36) at(newpolitym
> in1=-5 newpolitymin1sq=25) at(newpolitymin1=-4 newpolitymin1sq=16) at(newpolitymin1=-3 newpolitymin1sq=9) at(new
> politymin1=-2 newpolitymin1sq=4) at(newpolitymin1=-1 newpolitymin1sq=1) at(newpolitymin1=0 newpolitymin1sq=0) at
> (newpolitymin1=1 newpolitymin1sq=1) at(newpolitymin1=2 newpolitymin1sq=4) at(newpolitymin1=3 newpolitymin1sq=9) 
> at(newpolitymin1=4 newpolitymin1sq=16) at(newpolitymin1=5 newpolitymin1sq=25) at(newpolitymin1=6 newpolitymin1sq
> =36) at(newpolitymin1=7 newpolitymin1sq=49) at(newpolitymin1=8 newpolitymin1sq=64) at(newpolitymin1=9 newpolitym
> in1sq=81) at(newpolitymin1=10 newpolitymin1sq=100) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        165
                                                Average RVI       =     0.0147
                                                Largest FMI       =     0.0816
DF adjustment:   Large sample                   DF:     min       =   2,895.37
                                                        avg       =  47,249.71
Within VCE type: Delta-method                           max       = 364,467.23

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =         -10
               newpolitym~q    =         100

2._at        : newpolitym~1    =          -9
               newpolitym~q    =          81

3._at        : newpolitym~1    =          -8
               newpolitym~q    =          64

4._at        : newpolitym~1    =          -7
               newpolitym~q    =          49

5._at        : newpolitym~1    =          -6
               newpolitym~q    =          36

6._at        : newpolitym~1    =          -5
               newpolitym~q    =          25

7._at        : newpolitym~1    =          -4
               newpolitym~q    =          16

8._at        : newpolitym~1    =          -3
               newpolitym~q    =           9

9._at        : newpolitym~1    =          -2
               newpolitym~q    =           4

10._at       : newpolitym~1    =          -1
               newpolitym~q    =           1

11._at       : newpolitym~1    =           0
               newpolitym~q    =           0

12._at       : newpolitym~1    =           1
               newpolitym~q    =           1

13._at       : newpolitym~1    =           2
               newpolitym~q    =           4

14._at       : newpolitym~1    =           3
               newpolitym~q    =           9

15._at       : newpolitym~1    =           4
               newpolitym~q    =          16

16._at       : newpolitym~1    =           5
               newpolitym~q    =          25

17._at       : newpolitym~1    =           6
               newpolitym~q    =          36

18._at       : newpolitym~1    =           7
               newpolitym~q    =          49

19._at       : newpolitym~1    =           8
               newpolitym~q    =          64

20._at       : newpolitym~1    =           9
               newpolitym~q    =          81

21._at       : newpolitym~1    =          10
               newpolitym~q    =         100

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3955926   .1008154     3.92   0.000       .19798    .5932053
          2  |   .4524112    .080954     5.59   0.000     .2937372    .6110853
          3  |   .5019599   .0638528     7.86   0.000     .3768093    .6271106
          4  |     .54294   .0528517    10.27   0.000     .4393522    .6465278
          5  |    .574925   .0490879    11.71   0.000     .4787131     .671137
          6  |   .5980103   .0506646    11.80   0.000     .4987037    .6973168
          7  |   .6124991   .0545711    11.22   0.000       .50553    .7194682
          8  |   .6186822   .0587648    10.53   0.000     .5034895    .7338749
          9  |   .6167091   .0622705     9.90   0.000     .4946438    .7387744
         10  |   .6065368   .0646634     9.38   0.000     .4797827    .7332909
         11  |   .5879437   .0657562     8.94   0.000     .4590511    .7168363
         12  |   .5606134   .0655386     8.55   0.000     .4321519    .6890749
         13  |   .5243009   .0643071     8.15   0.000     .3982573    .6503446
         14  |   .4790958   .0628935     7.62   0.000     .3558252    .6023664
         15  |   .4257638   .0626759     6.79   0.000     .3029199    .5486077
         16  |   .3660804   .0647999     5.65   0.000     .2390709    .4930898
         17  |   .3029784   .0688008     4.40   0.000     .1681188     .437838
         18  |   .2403018   .0723604     3.32   0.001     .0984517     .382152
         19  |   .1821001   .0728133     2.50   0.012     .0393487    .3248515
         20  |   .1316928   .0687688     1.92   0.056    -.0031398    .2665254
         21  |   .0909457   .0606169     1.50   0.134    -.0279109    .2098022
------------------------------------------------------------------------------

. * Outcomes for all urban civic episodes
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq  if startyear>1
> 899 & urbancivic==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =         54
                                                Average RVI       =     0.0204
                                                Largest FMI       =     0.0424
DF adjustment:   Large sample                   DF:     min       =  10,666.09
                                                        avg       =  39,656.02
                                                        max       =  96,974.46
Model F test:       Equal FMI                   F(   2,38595.4)   =       2.39
Within VCE type:          OIM                   Prob > F          =     0.0920

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |   .9403654   .0516946    -1.12   0.263     .8443122    1.047346
newpolitymin1sq |   .9680189   .0155061    -2.03   0.042     .9380962    .9988961
          _cons |   4.800807   3.371747     2.23   0.026     1.211788    19.01962
---------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=-10 newpolitymin1sq=100) at(newpolitymin1=-9 newpolitymin1sq=81) at(newpolitymin1=-8 n
> ewpolitymin1sq=64) at(newpolitymin1=-7 newpolitymin1sq=49) at(newpolitymin1=-6 newpolitymin1sq=36) at(newpolitym
> in1=-5 newpolitymin1sq=25) at(newpolitymin1=-4 newpolitymin1sq=16) at(newpolitymin1=-3 newpolitymin1sq=9) at(new
> politymin1=-2 newpolitymin1sq=4) at(newpolitymin1=-1 newpolitymin1sq=1) at(newpolitymin1=0 newpolitymin1sq=0) at
> (newpolitymin1=1 newpolitymin1sq=1) at(newpolitymin1=2 newpolitymin1sq=4) at(newpolitymin1=3 newpolitymin1sq=9) 
> at(newpolitymin1=4 newpolitymin1sq=16) at(newpolitymin1=5 newpolitymin1sq=25) at(newpolitymin1=6 newpolitymin1sq
> =36) at(newpolitymin1=7 newpolitymin1sq=49) at(newpolitymin1=8 newpolitymin1sq=64) at(newpolitymin1=9 newpolitym
> in1sq=81) at(newpolitymin1=10 newpolitymin1sq=100) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =         54
                                                Average RVI       =     0.0084
                                                Largest FMI       =     0.0455
DF adjustment:   Large sample                   DF:     min       =   9,267.94
                                                        avg       =  63,207.08
Within VCE type: Delta-method                           max       = 337,634.41

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =         -10
               newpolitym~q    =         100

2._at        : newpolitym~1    =          -9
               newpolitym~q    =          81

3._at        : newpolitym~1    =          -8
               newpolitym~q    =          64

4._at        : newpolitym~1    =          -7
               newpolitym~q    =          49

5._at        : newpolitym~1    =          -6
               newpolitym~q    =          36

6._at        : newpolitym~1    =          -5
               newpolitym~q    =          25

7._at        : newpolitym~1    =          -4
               newpolitym~q    =          16

8._at        : newpolitym~1    =          -3
               newpolitym~q    =           9

9._at        : newpolitym~1    =          -2
               newpolitym~q    =           4

10._at       : newpolitym~1    =          -1
               newpolitym~q    =           1

11._at       : newpolitym~1    =           0
               newpolitym~q    =           0

12._at       : newpolitym~1    =           1
               newpolitym~q    =           1

13._at       : newpolitym~1    =           2
               newpolitym~q    =           4

14._at       : newpolitym~1    =           3
               newpolitym~q    =           9

15._at       : newpolitym~1    =           4
               newpolitym~q    =          16

16._at       : newpolitym~1    =           5
               newpolitym~q    =          25

17._at       : newpolitym~1    =           6
               newpolitym~q    =          36

18._at       : newpolitym~1    =           7
               newpolitym~q    =          49

19._at       : newpolitym~1    =           8
               newpolitym~q    =          64

20._at       : newpolitym~1    =           9
               newpolitym~q    =          81

21._at       : newpolitym~1    =          10
               newpolitym~q    =         100

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2573478   .1930996     1.33   0.183    -.1211489    .6358445
          2  |   .3753981   .1734161     2.16   0.030     .0354931    .7153031
          3  |   .4951107   .1304335     3.80   0.000     .2394609    .7507606
          4  |   .6002278   .0914979     6.56   0.000     .4208946    .7795609
          5  |    .682936   .0760282     8.98   0.000     .5339216    .8319504
          6  |   .7431814   .0781919     9.50   0.000     .5899197    .8964432
          7  |   .7845389   .0840286     9.34   0.000     .6198303    .9492476
          8  |    .811054   .0887675     9.14   0.000     .6370524    .9850557
          9  |    .825868   .0924867     8.93   0.000     .6445742    1.007162
         10  |   .8308778   .0961023     8.65   0.000     .6424962    1.019259
         11  |   .8267504   .1003026     8.24   0.000     .6301359    1.023365
         12  |   .8129621    .105376     7.71   0.000     .6064048    1.019519
         13  |    .787771   .1112024     7.08   0.000     .5697968    1.005745
         14  |   .7481933   .1173469     6.38   0.000     .5181806    .9782061
         15  |   .6902682   .1235721     5.59   0.000      .448061    .9324753
         16  |   .6102373   .1311066     4.65   0.000     .3532691    .8672056
         17  |     .50743   .1428208     3.55   0.000      .227505    .7873551
         18  |   .3884409   .1570892     2.47   0.013     .0805498    .6963319
         19  |   .2687229   .1617402     1.66   0.097    -.0482876    .5857334
         20  |   .1665884   .1454855     1.15   0.252    -.1185685    .4517452
         21  |   .0929212   .1122044     0.83   0.408    -.1270087    .3128511
------------------------------------------------------------------------------

. * Outcomes for all social revolutionary episodes
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq  if startyear>1
> 899 & leftist==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =         67
                                                Average RVI       =     0.1108
                                                Largest FMI       =     0.1807
DF adjustment:   Large sample                   DF:     min       =     600.02
                                                        avg       =   1,040.34
                                                        max       =   1,352.27
Model F test:       Equal FMI                   F(   2, 1620.7)   =       5.54
Within VCE type:          OIM                   Prob > F          =     0.0040

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |    .803443   .0662225    -2.66   0.008     .6834763    .9444669
newpolitymin1sq |   1.007505   .0142715     0.53   0.598     .9798633    1.035927
          _cons |   .1323924   .0850071    -3.15   0.002      .037569    .4665479
---------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=-10 newpolitymin1sq=100) at(newpolitymin1=-9 newpolitymin1sq=81) at(newpolitymin1=-8 n
> ewpolitymin1sq=64) at(newpolitymin1=-7 newpolitymin1sq=49) at(newpolitymin1=-6 newpolitymin1sq=36) at(newpolitym
> in1=-5 newpolitymin1sq=25) at(newpolitymin1=-4 newpolitymin1sq=16) at(newpolitymin1=-3 newpolitymin1sq=9) at(new
> politymin1=-2 newpolitymin1sq=4) at(newpolitymin1=-1 newpolitymin1sq=1) at(newpolitymin1=0 newpolitymin1sq=0) at
> (newpolitymin1=1 newpolitymin1sq=1) at(newpolitymin1=2 newpolitymin1sq=4) at(newpolitymin1=3 newpolitymin1sq=9) 
> at(newpolitymin1=4 newpolitymin1sq=16) at(newpolitymin1=5 newpolitymin1sq=25) at(newpolitymin1=6 newpolitymin1sq
> =36) at(newpolitymin1=7 newpolitymin1sq=49) at(newpolitymin1=8 newpolitymin1sq=64) at(newpolitymin1=9 newpolitym
> in1sq=81) at(newpolitymin1=10 newpolitymin1sq=100) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =         67
                                                Average RVI       =     0.0668
                                                Largest FMI       =     0.1926
DF adjustment:   Large sample                   DF:     min       =     528.71
                                                        avg       =   5,382.13
Within VCE type: Delta-method                           max       =  47,416.52

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =         -10
               newpolitym~q    =         100

2._at        : newpolitym~1    =          -9
               newpolitym~q    =          81

3._at        : newpolitym~1    =          -8
               newpolitym~q    =          64

4._at        : newpolitym~1    =          -7
               newpolitym~q    =          49

5._at        : newpolitym~1    =          -6
               newpolitym~q    =          36

6._at        : newpolitym~1    =          -5
               newpolitym~q    =          25

7._at        : newpolitym~1    =          -4
               newpolitym~q    =          16

8._at        : newpolitym~1    =          -3
               newpolitym~q    =           9

9._at        : newpolitym~1    =          -2
               newpolitym~q    =           4

10._at       : newpolitym~1    =          -1
               newpolitym~q    =           1

11._at       : newpolitym~1    =           0
               newpolitym~q    =           0

12._at       : newpolitym~1    =           1
               newpolitym~q    =           1

13._at       : newpolitym~1    =           2
               newpolitym~q    =           4

14._at       : newpolitym~1    =           3
               newpolitym~q    =           9

15._at       : newpolitym~1    =           4
               newpolitym~q    =          16

16._at       : newpolitym~1    =           5
               newpolitym~q    =          25

17._at       : newpolitym~1    =           6
               newpolitym~q    =          36

18._at       : newpolitym~1    =           7
               newpolitym~q    =          49

19._at       : newpolitym~1    =           8
               newpolitym~q    =          64

20._at       : newpolitym~1    =           9
               newpolitym~q    =          81

21._at       : newpolitym~1    =          10
               newpolitym~q    =         100

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .711636   .1579815     4.50   0.000     .4018379    1.021434
          2  |   .6342969   .1363568     4.65   0.000     .3669794    .9016145
          3  |   .5515898   .1142828     4.83   0.000     .3275896      .77559
          4  |   .4691497   .1013166     4.63   0.000     .2705677    .6677316
          5  |   .3920384   .0993673     3.95   0.000     .1972473    .5868295
          6  |   .3235327   .1012632     3.19   0.001     .1249632    .5221021
          7  |   .2649639   .1006652     2.63   0.009     .0675182    .4624097
          8  |   .2162288   .0957686     2.26   0.024     .0283643    .4040934
          9  |   .1764219   .0873803     2.02   0.044     .0050067    .3478372
         10  |   .1443137   .0770809     1.87   0.061    -.0068935     .295521
         11  |   .1186364   .0663746     1.79   0.074    -.0115615    .2488343
         12  |   .0982267   .0564203     1.74   0.082    -.0124366      .20889
         13  |   .0820835   .0480144     1.71   0.088    -.0120839    .1762509
         14  |   .0693789   .0416529     1.67   0.096    -.0123058    .1510637
         15  |    .059448   .0375848     1.58   0.114    -.0142569    .1331528
         16  |   .0517704     .03584     1.44   0.149    -.0185175    .1220582
         17  |   .0459515   .0362954     1.27   0.206    -.0252435    .1171465
         18  |   .0417062   .0388388     1.07   0.283    -.0345032    .1179156
         19  |   .0388479   .0435819     0.89   0.373    -.0467044    .1244003
         20  |   .0372838   .0510372     0.73   0.465    -.0629458    .1375133
         21  |   .0370164   .0622488     0.59   0.552    -.0852689    .1593017
------------------------------------------------------------------------------

. 
. * =========================
. * Results on regime types
. * =========================
. * Regime-types
. * Use complete-case sample, as data available for all cases
. clear

. use revolutionaryeps.dta

. * Test on regime-types, excluding colonies
. logit success ib(#5).new2incumbgovtype if startyear>1899 & colony==0

Iteration 0:   log likelihood = -191.03364  
Iteration 1:   log likelihood = -184.43352  
Iteration 2:   log likelihood =  -184.3469  
Iteration 3:   log likelihood = -184.34687  
Iteration 4:   log likelihood = -184.34687  

Logistic regression                             Number of obs     =        288
                                                LR chi2(5)        =      13.37
                                                Prob > chi2       =     0.0201
Log likelihood = -184.34687                     Pseudo R2         =     0.0350

----------------------------------------------------------------------------------------
               success |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     new2incumbgovtype |
             Monarchy  |   1.544899   .5482657     2.82   0.005     .4703184     2.61948
        Military govt  |   1.560648    .518698     3.01   0.003     .5440183    2.577277
One-party auth regime  |   1.294357   .4852758     2.67   0.008     .3432337     2.24548
   Competitive author  |   1.104882   .4706873     2.35   0.019     .1823518    2.027412
                Other  |   .8556661   .5960303     1.44   0.151    -.3125318    2.023864
                       |
                 _cons |  -1.609438   .4140393    -3.89   0.000     -2.42094   -.7979357
----------------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |       109           179  |        288
-----------+--------------------------+-----------
   Total   |       109           179  |        288

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   62.15%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.00%
False - rate for true D         Pr( -| D)  100.00%
False + rate for classified +   Pr(~D| +)       .%
False - rate for classified -   Pr( D| -)   37.85%
--------------------------------------------------
Correctly classified                        62.15%
--------------------------------------------------

. margins i.new2incumbgovtype

Adjusted predictions                            Number of obs     =        288
Model VCE    : OIM

Expression   : Pr(success), predict()

----------------------------------------------------------------------------------------
                       |            Delta-method
                       |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
     new2incumbgovtype |
             Monarchy  |    .483871   .0897559     5.39   0.000     .3079526    .6597893
        Military govt  |   .4878049   .0780637     6.25   0.000     .3348029    .6408068
One-party auth regime  |    .421875   .0617323     6.83   0.000     .3008818    .5428682
   Competitive author  |   .3764706   .0525514     7.16   0.000     .2734717    .4794695
            Democracy  |   .1666667   .0575055     2.90   0.004      .053958    .2793753
                Other  |        .32   .0932952     3.43   0.001     .1371447    .5028553
----------------------------------------------------------------------------------------

. * Magnification of threat to all regime types if episode occurs in cities
. logit success i.urbandum##ib(#5).new2incumbgovtype if startyear>1899 & colony==0, or nolog 

Logistic regression                             Number of obs     =        288
                                                LR chi2(11)       =      30.65
                                                Prob > chi2       =     0.0013
Log likelihood = -175.70934                     Pseudo R2         =     0.0802

--------------------------------------------------------------------------------------------
                   success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
                  urbandum |
                      yes  |          8   9.062744     1.84   0.066     .8685849    73.68307
                           |
         new2incumbgovtype |
                 Monarchy  |        7.5   9.211067     1.64   0.101     .6755612    83.26411
            Military govt  |   10.90909   12.47447     2.09   0.037     1.159967    102.5962
    One-party auth regime  |         10    11.1243     2.07   0.038     1.130051    88.49158
       Competitive author  |   6.428571   7.032826     1.70   0.089     .7531892    54.86872
                    Other  |          6    7.30753     1.47   0.141     .5513843    65.29022
                           |
urbandum#new2incumbgovtype |
             yes#Monarchy  |         .5    .698212    -0.50   0.620     .0323841     7.71984
        yes#Military govt  |   .3208333   .4198307    -0.87   0.385     .0246847    4.169947
yes#One-party auth regime  |   .2261905   .2835137    -1.19   0.236     .0193889     2.63873
   yes#Competitive author  |   .3577778   .4401738    -0.84   0.403     .0320908    3.988837
                yes#Other  |    .297619   .4271143    -0.84   0.398     .0178689    4.957053
                           |
                     _cons |        .05   .0512348    -2.92   0.003     .0067104    .3725564
--------------------------------------------------------------------------------------------

. margins urbandum#new2incumbgovtype

Adjusted predictions                            Number of obs     =        288
Model VCE    : OIM

Expression   : Pr(success), predict()

--------------------------------------------------------------------------------------------
                           |            Delta-method
                           |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
urbandum#new2incumbgovtype |
              no#Monarchy  |   .2727273   .1342816     2.03   0.042     .0095401    .5359144
         no#Military govt  |   .3529412    .115904     3.05   0.002     .1257734    .5801089
 no#One-party auth regime  |   .3333333    .096225     3.46   0.001     .1447357     .521931
    no#Competitive author  |   .2432432   .0705339     3.45   0.001     .1049994    .3814871
             no#Democracy  |    .047619   .0464714     1.02   0.306    -.0434633    .1387014
                 no#Other  |   .2307692   .1168545     1.97   0.048     .0017385    .4597999
             yes#Monarchy  |         .6   .1095445     5.48   0.000     .3852967    .8147033
        yes#Military govt  |   .5833333   .1006346     5.80   0.000     .3860932    .7805734
yes#One-party auth regime  |       .475   .0789581     6.02   0.000      .320245     .629755
   yes#Competitive author  |   .4791667   .0721061     6.65   0.000     .3378413     .620492
            yes#Democracy  |   .2857143   .0985808     2.90   0.004     .0924995    .4789291
                yes#Other  |   .4166667   .1423188     2.93   0.003      .137727    .6956063
--------------------------------------------------------------------------------------------

. 
. *  Yrs leader in power
. * Use imputed sample on all episodes, excluding colonies
. clear

. use revolutionaryepsmireg.dta

. set seed 1234

. * Estimate effect of yrs leader in power on success
. mi estimate, post dots eform saving(miest, replace):  logit success newincumbpowerdur if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0016
                                                Largest FMI       =     0.0029
DF adjustment:   Large sample                   DF:     min       = 2188529.58
                                                        avg       =   1.87e+07
                                                        max       =   3.53e+07
Model F test:       Equal FMI                   F(   1, 2.2e+06)  =      13.68
Within VCE type:          OIM                   Prob > F          =     0.0002

-----------------------------------------------------------------------------------
          success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
newincumbpowerdur |   1.054869   .0152353     3.70   0.000     1.025427    1.085156
            _cons |   .4011127   .0675773    -5.42   0.000     .2883107    .5580488
-----------------------------------------------------------------------------------

. mimrgns, atmeans at(newincumbpowerdur=(0 5 10 15 20 25 30 35)) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0012
                                                Largest FMI       =     0.0031
DF adjustment:   Large sample                   DF:     min       = 1922476.52
                                                        avg       =   3.04e+09
Within VCE type: Delta-method                           max       =   2.42e+10

Expression   : Pr(success), predict(pr)

1._at        : newincumbp~r    =           0

2._at        : newincumbp~r    =           5

3._at        : newincumbp~r    =          10

4._at        : newincumbp~r    =          15

5._at        : newincumbp~r    =          20

6._at        : newincumbp~r    =          25

7._at        : newincumbp~r    =          30

8._at        : newincumbp~r    =          35

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2862824   .0344235     8.32   0.000     .2188135    .3537512
          2  |   .3437942   .0296294    11.60   0.000     .2857216    .4018667
          3  |   .4062839   .0310266    13.09   0.000     .3454729    .4670949
          4  |   .4719626   .0403918    11.68   0.000      .392796    .5511291
          5  |    .538625   .0535425    10.06   0.000     .4336836    .6435663
          6  |   .6039313   .0663744     9.10   0.000     .4738399    .7340227
          7  |    .665727   .0764435     8.71   0.000     .5159003    .8155537
          8  |   .7223105   .0825618     8.75   0.000     .5604922    .8841289
------------------------------------------------------------------------------

. 
. *  Personalist regimes using Geddes et al. classification (since 1945 only, complete case sample)
. * Use complete case sample
. clear

. use revolutionaryeps.dta

. *  Analyze impact of personalist regime on success
. logit success  gedincpersonal if startyear>1945, or nolog 

Logistic regression                             Number of obs     =        139
                                                LR chi2(1)        =       4.77
                                                Prob > chi2       =     0.0290
Log likelihood = -93.670714                     Pseudo R2         =     0.0248

--------------------------------------------------------------------------------
       success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
gedincpersonal |   2.302439   .8907575     2.16   0.031     1.078646    4.914703
         _cons |   .6949153   .1412908    -1.79   0.073     .4665141     1.03514
--------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        24            15  |         39
     -     |        41            59  |        100
-----------+--------------------------+-----------
   Total   |        65            74  |        139

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   36.92%
Specificity                     Pr( -|~D)   79.73%
Positive predictive value       Pr( D| +)   61.54%
Negative predictive value       Pr(~D| -)   59.00%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   20.27%
False - rate for true D         Pr( -| D)   63.08%
False + rate for classified +   Pr(~D| +)   38.46%
False - rate for classified -   Pr( D| -)   41.00%
--------------------------------------------------
Correctly classified                        59.71%
--------------------------------------------------

. margins, at(gedincpersonal=(0 1))

Adjusted predictions                            Number of obs     =        139
Model VCE    : OIM

Expression   : Pr(success), predict()

1._at        : gedincpers~l    =           0

2._at        : gedincpers~l    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |        .41   .0491833     8.34   0.000     .3136024    .5063976
          2  |   .6153846    .077903     7.90   0.000     .4626975    .7680717
------------------------------------------------------------------------------

. * T-test of yrs incumbent in power for regimes experiencing episode (personalist vs. other regimes)
. ttest incumbpowerdur, by(gedincpersonal)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      99    7.575758    .8541423    8.498608     5.88074    9.270775
       1 |      39    11.23077    1.560071     9.74264    8.072571    14.38897
---------+--------------------------------------------------------------------
combined |     138    8.608696    .7648135    8.984524     7.09633    10.12106
---------+--------------------------------------------------------------------
    diff |           -3.655012    1.675751               -6.968912   -.3411115
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.1811
Ho: diff = 0                                     degrees of freedom =      136

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0154         Pr(|T| > |t|) = 0.0309          Pr(T > t) = 0.9846

. 
. * ========================================================
. * Results on age of incumbent leader in autocratic regimes
. * ========================================================
. * Use imputed sample on all episodes, excluding colonies
. clear

. use revolutionaryepsmireg.dta

. set seed 1234

. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbage if
>  startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0308
                                                Largest FMI       =     0.0833
DF adjustment:   Large sample                   DF:     min       =   2,784.48
                                                        avg       = 412,629.90
                                                        max       = 830,351.91
Model F test:       Equal FMI                   F(   3,32538.1)   =       7.33
Within VCE type:          OIM                   Prob > F          =     0.0001

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |   .8898401   .0242242    -4.29   0.000     .8436004    .9386144
newpolitymin1sq |   .9853736   .0052156    -2.78   0.005     .9751996    .9956537
   newincumbage |   1.023701   .0112813     2.13   0.034     1.001827    1.046052
          _cons |   .2248488   .1417501    -2.37   0.018     .0653539    .7735873
---------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=-6 newpolitymin1sq=36 newincumbage=40) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0057
                                                Largest FMI       =     0.0057
DF adjustment:   Large sample                   DF:     min       = 589,495.80
                                                        avg       = 589,495.80
Within VCE type: Delta-method                           max       = 589,495.80

Expression   : Pr(success), predict(pr)
at           : newpolitym~1    =          -6
               newpolitym~q    =          36
               newincumbage    =          40

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4048059   .0542504     7.46   0.000     .2984767     .511135
------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=-6 newpolitymin1sq=36 newincumbage=75) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0115
                                                Largest FMI       =     0.0114
DF adjustment:   Large sample                   DF:     min       = 147,349.03
                                                        avg       = 147,349.03
Within VCE type: Delta-method                           max       = 147,349.03

Expression   : Pr(success), predict(pr)
at           : newpolitym~1    =          -6
               newpolitym~q    =          36
               newincumbage    =          75

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .6068936   .0650261     9.33   0.000     .4794437    .7343435
------------------------------------------------------------------------------

. mimrgns, at(newpolitymin1=6 newpolitymin1sq=36 newincumbage=75) predict (pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0189
                                                Largest FMI       =     0.0186
DF adjustment:   Large sample                   DF:     min       =  55,147.25
                                                        avg       =  55,147.25
Within VCE type: Delta-method                           max       =  55,147.25

Expression   : Pr(success), predict(pr)
at           : newpolitym~1    =           6
               newpolitym~q    =          36
               newincumbage    =          75

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2757162   .0645434     4.27   0.000     .1492107    .4022218
------------------------------------------------------------------------------

. * Controlling for number of years incumbent in office
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbage ne
> wincumbpowerdur if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0334
                                                Largest FMI       =     0.1090
DF adjustment:   Large sample                   DF:     min       =   1,632.73
                                                        avg       = 355,404.80
                                                        max       = 1140137.79
Model F test:       Equal FMI                   F(   4,43275.1)   =       6.80
Within VCE type:          OIM                   Prob > F          =     0.0000

-----------------------------------------------------------------------------------
          success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
    newpolitymin1 |   .9057748   .0257951    -3.48   0.001     .8565954    .9577777
  newpolitymin1sq |   .9828891   .0054897    -3.09   0.002     .9721803    .9937158
     newincumbage |   1.013947   .0117277     1.20   0.231     .9912197    1.037196
newincumbpowerdur |    1.04549   .0174759     2.66   0.008     1.011792    1.080311
            _cons |   .3068681   .1961401    -1.85   0.065     .0876779    1.074022
-----------------------------------------------------------------------------------

. * Age of leader and yrs incumbent in power only moderately correlated
. mi xeq 1: corr newincumbage newincumbpowerdur

m=1 data:
-> corr newincumbage newincumbpowerdur
(obs=288)

             | newin~ge newinc~r
-------------+------------------
newincumbage |   1.0000
newincumbp~r |   0.2923   1.0000


. 
. * ====================================================================
. * Results on level of development, oil, military spending per soldier
. * ====================================================================
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0798
                                                Largest FMI       =     0.2120
DF adjustment:   Large sample                   DF:     min       =     437.43
                                                        avg       =   4,301.25
                                                        max       =  14,659.81
Model F test:       Equal FMI                   F(   8,22005.8)   =       6.05
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .8954253   .0284403    -3.48   0.001     .8413614    .9529631
    newpolitymin1sq |   .9784535   .0062559    -3.41   0.001     .9662528    .9908082
  newincumbpowerdur |   1.045536   .0182613     2.55   0.011     1.010346    1.081951
        newgdppcthl |   .8503554   .0665271    -2.07   0.038     .7294369    .9913185
          newlnoill |   .8865447   .0321929    -3.32   0.001     .8256359    .9519469
newmilexpsold10tile |   1.468702   .1358766     4.15   0.000     1.224627    1.761422
           civilwar |   1.031639   .7203229     0.04   0.964     .2618121    4.065053
      newcivxmilexp |   .7980859   .0971743    -1.85   0.065     .6282326    1.013862
              _cons |   .3501831    .162551    -2.26   0.024     .1409348     .870106
-------------------------------------------------------------------------------------

. mimrgns, atmeans at(newgdppcthl=(1 5 10)) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0399
                                                Largest FMI       =     0.0739
DF adjustment:   Large sample                   DF:     min       =   3,532.77
                                                        avg       =  16,485.70
Within VCE type: Delta-method                           max       =  41,395.06

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =           1
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =    5.531778 (mean)
               civilwar        =    .4791667 (mean)
               newcivxmil~p    =    2.524479 (mean)

2._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =           5
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =    5.531778 (mean)
               civilwar        =    .4791667 (mean)
               newcivxmil~p    =    2.524479 (mean)

3._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =          10
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =    5.531778 (mean)
               civilwar        =    .4791667 (mean)
               newcivxmil~p    =    2.524479 (mean)

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3923862   .0465739     8.43   0.000     .3011003     .483672
          2  |   .2525782    .046382     5.45   0.000     .1616469    .3435095
          3  |   .1315313   .0689317     1.91   0.056    -.0036187    .2666813
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. mimrgns, atmeans at((p10) newlnoill) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0076
                                                Largest FMI       =     0.0075
DF adjustment:   Large sample                   DF:     min       = 336,597.04
                                                        avg       = 336,597.04
Within VCE type: Delta-method                           max       = 336,597.04

Expression   : Pr(success), predict(pr)
at           : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =           0 (p10)
               newmilexps~e    =    5.531778 (mean)
               civilwar        =    .4791667 (mean)
               newcivxmil~p    =    2.524479 (mean)

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4370032   .0453864     9.63   0.000     .3480472    .5259591
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. mimrgns, atmeans at((p90) newlnoill) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0456
                                                Largest FMI       =     0.0438
DF adjustment:   Large sample                   DF:     min       =   9,969.42
                                                        avg       =   9,969.42
Within VCE type: Delta-method                           max       =   9,969.42

Expression   : Pr(success), predict(pr)
at           : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    10.88438 (p90)
               newmilexps~e    =    5.531778 (mean)
               civilwar        =    .4791667 (mean)
               newcivxmil~p    =    2.524479 (mean)

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1732543   .0465728     3.72   0.000     .0819622    .2645465
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. * T-tests of GDP per capita/oil and urban location of episodes
. mi xeq 1: ttest newgdppcthl if startyear>1899, by(urbandum)

m=1 data:
-> ttest newgdppcthl if startyear>1899, by(urbandum)

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      no |     123    1.560356    .1453397    1.611895    1.272642    1.848071
     yes |     165    3.569194    .2212025    2.841398    3.132422    4.005966
---------+--------------------------------------------------------------------
combined |     288    2.711253    .1526247    2.590127    2.410847    3.011658
---------+--------------------------------------------------------------------
    diff |           -2.008838    .2853504               -2.570491   -1.447184
------------------------------------------------------------------------------
    diff = mean(no) - mean(yes)                                   t =  -7.0399
Ho: diff = 0                                     degrees of freedom =      286

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. mi xeq 1: ttest newlnoill if startyear>1899, by(urbandum)

m=1 data:
-> ttest newlnoill if startyear>1899, by(urbandum)

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      no |     123    3.328663    .4010346    4.447689    2.534775    4.122551
     yes |     165    4.184929    .3566572    4.581344    3.480697    4.889161
---------+--------------------------------------------------------------------
combined |     288    3.819232    .2673339    4.536807    3.293048    4.345416
---------+--------------------------------------------------------------------
    diff |           -.8562655    .5390169               -1.917209    .2046778
------------------------------------------------------------------------------
    diff = mean(no) - mean(yes)                                   t =  -1.5886
Ho: diff = 0                                     degrees of freedom =      286

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0566         Pr(|T| > |t|) = 0.1133          Pr(T > t) = 0.9434

. * Predictions for Haiti in 1946
. mimrgns, atmeans at(newgdppcthl=1.046 newlnoill=0) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0383
                                                Largest FMI       =     0.0370
DF adjustment:   Large sample                   DF:     min       =  13,974.78
                                                        avg       =  13,974.78
Within VCE type: Delta-method                           max       =  13,974.78

Expression   : Pr(success), predict(pr)
at           : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =       1.046
               newlnoill       =           0
               newmilexps~e    =    5.531778 (mean)
               civilwar        =    .4791667 (mean)
               newcivxmil~p    =    2.524479 (mean)

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5044618   .0531498     9.49   0.000     .4002811    .6086424
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. * Predictions for Azerbaijan in 2005
. mimrgns, atmeans at(newgdppcthl=4.596682 newlnoill=9.660559) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0821
                                                Largest FMI       =     0.0764
DF adjustment:   Large sample                   DF:     min       =   3,299.78
                                                        avg       =   3,299.78
Within VCE type: Delta-method                           max       =   3,299.78

Expression   : Pr(success), predict(pr)
at           : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    4.596682
               newlnoill       =    9.660559
               newmilexps~e    =    5.531778 (mean)
               civilwar        =    .4791667 (mean)
               newcivxmil~p    =    2.524479 (mean)

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1520483   .0414623     3.67   0.000     .0707539    .2333428
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. 
. * =============================================================================
. * FIGURE 4.5 on interaction between military spending per soldier and civil war
. * =============================================================================
. * Interaction between military spending per soldier and civil war, controlling for GDP per capital and oil produ
> ction
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0798
                                                Largest FMI       =     0.2120
DF adjustment:   Large sample                   DF:     min       =     437.43
                                                        avg       =   4,301.25
                                                        max       =  14,659.81
Model F test:       Equal FMI                   F(   8,22005.8)   =       6.05
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .8954253   .0284403    -3.48   0.001     .8413614    .9529631
    newpolitymin1sq |   .9784535   .0062559    -3.41   0.001     .9662528    .9908082
  newincumbpowerdur |   1.045536   .0182613     2.55   0.011     1.010346    1.081951
        newgdppcthl |   .8503554   .0665271    -2.07   0.038     .7294369    .9913185
          newlnoill |   .8865447   .0321929    -3.32   0.001     .8256359    .9519469
newmilexpsold10tile |   1.468702   .1358766     4.15   0.000     1.224627    1.761422
           civilwar |   1.031639   .7203229     0.04   0.964     .2618121    4.065053
      newcivxmilexp |   .7980859   .0971743    -1.85   0.065     .6282326    1.013862
              _cons |   .3501831    .162551    -2.26   0.024     .1409348     .870106
-------------------------------------------------------------------------------------

. mimrgns, atmeans at(civilwar=0 newmilexpsold10tile=1 newcivxmilexp=0) at(civilwar=0 newmilexpsold10tile=2 newciv
> xmilexp=0) at(civilwar=0 newmilexpsold10tile=3 newcivxmilexp=0) at(civilwar=0 newmilexpsold10tile=4 newcivxmilex
> p=0) at(civilwar=0 newmilexpsold10tile=5 newcivxmilexp=0) at(civilwar=0 newmilexpsold10tile=6 newcivxmilexp=0) a
> t(civilwar=0 newmilexpsold10tile=7 newcivxmilexp=0) at(civilwar=0 newmilexpsold10tile=8 newcivxmilexp=0) at(civi
> lwar=0 newmilexpsold10tile=9 newcivxmilexp=0) at(civilwar=0 newmilexpsold10tile=10 newcivxmilexp=0) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0487
                                                Largest FMI       =     0.1657
DF adjustment:   Large sample                   DF:     min       =     712.09
                                                        avg       =   8,115.63
Within VCE type: Delta-method                           max       =  43,063.99

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           1
               civilwar        =           0
               newcivxmil~p    =           0

2._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           2
               civilwar        =           0
               newcivxmil~p    =           0

3._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           3
               civilwar        =           0
               newcivxmil~p    =           0

4._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           4
               civilwar        =           0
               newcivxmil~p    =           0

5._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           5
               civilwar        =           0
               newcivxmil~p    =           0

6._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           6
               civilwar        =           0
               newcivxmil~p    =           0

7._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           7
               civilwar        =           0
               newcivxmil~p    =           0

8._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           8
               civilwar        =           0
               newcivxmil~p    =           0

9._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           9
               civilwar        =           0
               newcivxmil~p    =           0

10._at       : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =          10
               civilwar        =           0
               newcivxmil~p    =           0

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .131084   .0530867     2.47   0.014     .0268588    .2353092
          2  |   .1805765   .0568158     3.18   0.002     .0690642    .2920888
          3  |   .2438596   .0569773     4.28   0.000     .1320808    .3556384
          4  |   .3210038   .0536972     5.98   0.000     .2157179    .4262898
          5  |   .4096396   .0500621     8.18   0.000     .3115148    .5077644
          6  |   .5047049   .0512214     9.85   0.000     .4043101    .6050998
          7  |   .5993745   .0578842    10.35   0.000     .4858925    .7128565
          8  |   .6869493   .0649337    10.58   0.000     .5595846     .814314
          9  |   .7626675   .0680331    11.21   0.000     .6291647    .8961703
         10  |   .8244723   .0661502    12.46   0.000     .6946238    .9543208
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. mimrgns, atmeans at(civilwar=1 newmilexpsold10tile=1 newcivxmilexp=1) at(civilwar=1 newmilexpsold10tile=2 newciv
> xmilexp=2) at(civilwar=1 newmilexpsold10tile=3 newcivxmilexp=3) at(civilwar=1 newmilexpsold10tile=4 newcivxmilex
> p=4) at(civilwar=1 newmilexpsold10tile=5 newcivxmilexp=5) at(civilwar=1 newmilexpsold10tile=6 newcivxmilexp=6) a
> t(civilwar=1 newmilexpsold10tile=7 newcivxmilexp=7) at(civilwar=1 newmilexpsold10tile=8 newcivxmilexp=8) at(civi
> lwar=1 newmilexpsold10tile=9 newcivxmilexp=9) at(civilwar=1 newmilexpsold10tile=10 newcivxmilexp=10) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0507
                                                Largest FMI       =     0.1292
DF adjustment:   Large sample                   DF:     min       =   1,164.31
                                                        avg       =   3,005.79
Within VCE type: Delta-method                           max       =   7,152.74

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           1
               civilwar        =           1
               newcivxmil~p    =           1

2._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           2
               civilwar        =           1
               newcivxmil~p    =           2

3._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           3
               civilwar        =           1
               newcivxmil~p    =           3

4._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           4
               civilwar        =           1
               newcivxmil~p    =           4

5._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           5
               civilwar        =           1
               newcivxmil~p    =           5

6._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           6
               civilwar        =           1
               newcivxmil~p    =           6

7._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           7
               civilwar        =           1
               newcivxmil~p    =           7

8._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           8
               civilwar        =           1
               newcivxmil~p    =           8

9._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =           9
               civilwar        =           1
               newcivxmil~p    =           9

10._at       : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newlnoill       =    3.841717 (mean)
               newmilexps~e    =          10
               civilwar        =           1
               newcivxmil~p    =          10

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1103617   .0469891     2.35   0.019      .018169    .2025545
          2  |   .1265168   .0447652     2.83   0.005     .0387025     .214331
          3  |    .144787   .0420673     3.44   0.001     .0622848    .2272893
          4  |   .1653364   .0396966     4.17   0.000     .0875041    .2431688
          5  |   .1882994   .0392444     4.80   0.000     .1113675    .2652312
          6  |   .2137646   .0428286     4.99   0.000     .1298078    .2977214
          7  |   .2417581   .0517783     4.67   0.000     .1402421    .3432741
          8  |    .272226   .0658382     4.13   0.000     .1431119    .4013401
          9  |   .3050215   .0839219     3.63   0.000     .1404033    .4696398
         10  |   .3398971   .1048388     3.24   0.001     .1342088    .5455855
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. * Favorability of revolutionary civil wars fought by conventional armies to opposition
. clear

. use revolutionaryeps.dta

. logit success  conventional  if startyear>1899 & civilwar==1, or nolog

Logistic regression                             Number of obs     =        174
                                                LR chi2(1)        =       3.80
                                                Prob > chi2       =     0.0513
Log likelihood =  -100.5876                     Pseudo R2         =     0.0185

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
conventional |   3.457143   2.183045     1.96   0.049     1.002809    11.91836
       _cons |   .3471074   .0621642    -5.91   0.000     .2443546    .4930687
------------------------------------------------------------------------------

. 
. * ===============================================================
. * Results on transnational revolutionary waves, political reform, 
. *   electoral revolution, external war
. * ===============================================================
. * Bivariate relationships
. clear

. use revolutionaryepsmireg.dta

. set seed 1234

. mi estimate, post dots eform saving(miest, replace): logit success newrevwaveny  if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =          .
                                                        avg       =          .
                                                        max       =          .
Model F test:       Equal FMI                   F(   1,      .)   =      11.05
Within VCE type:          OIM                   Prob > F          =     0.0009

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
newrevwaveny |   2.332864   .5945358     3.32   0.001     1.415659    3.844325
       _cons |   .4461538   .0704495    -5.11   0.000     .3273983    .6079849
------------------------------------------------------------------------------

. mimrgns,  at(newrevwaveny=(0 1)) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =          .
                                                        avg       =          .
Within VCE type: Delta-method                           max       =          .

Expression   : Pr(success), predict(pr)

1._at        : newrevwaveny    =           0

2._at        : newrevwaveny    =           1

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3085106    .033686     9.16   0.000     .2424874    .3745339
          2  |        .51     .04999    10.20   0.000     .4120214    .6079786
------------------------------------------------------------------------------

. * Waves, for urban revolutions only
. mi estimate, post dots eform saving(miest, replace): logit success newrevwaveny  if startyear>1899 & urbandum==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        165
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =   3.94e+63
                                                        avg       =   3.94e+63
                                                        max       =          .
Model F test:       Equal FMI                   F(   1,      .)   =       4.32
Within VCE type:          OIM                   Prob > F          =     0.0376

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
newrevwaveny |   1.928571   .6092981     2.08   0.038     1.038278    3.582266
       _cons |   .6666667   .1476025    -1.83   0.067      .431966    1.028888
------------------------------------------------------------------------------

. * Without control vars
. mi estimate, post dots eform saving(miest, replace): logit success newrevwaveny politreform electoralrev externa
> lwar if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =          .
                                                        avg       =          .
                                                        max       =          .
Model F test:       Equal FMI                   F(   4,      .)   =       3.99
Within VCE type:          OIM                   Prob > F          =     0.0031

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
newrevwaveny |   2.098994   .5533002     2.81   0.005     1.252081    3.518764
 politreform |    1.63281   .5608641     1.43   0.153     .8328199    3.201254
electoralrev |   1.821194   .6759125     1.62   0.106     .8799215    3.769369
 externalwar |   1.518255   .4729851     1.34   0.180     .8244568    2.795898
       _cons |   .3637201   .0672751    -5.47   0.000     .2531197    .5226474
------------------------------------------------------------------------------

. * With control vars
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newmilexpsold10tile newrevwaveny politreform electoralrev externalwar startyear if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0435
                                                Largest FMI       =     0.1876
DF adjustment:   Large sample                   DF:     min       =     557.15
                                                        avg       =  54,524.14
                                                        max       = 241,589.31
Model F test:       Equal FMI                   F(  10,93837.3)   =       4.31
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .9017405   .0274749    -3.39   0.001     .8494596     .957239
    newpolitymin1sq |   .9827863   .0057306    -2.98   0.003     .9716137    .9940874
  newincumbpowerdur |   1.047572   .0184992     2.63   0.009     1.011932    1.084468
        newgdppcthl |   .8880464   .0610261    -1.73   0.084     .7761301    1.016101
newmilexpsold10tile |   1.243825    .097482     2.78   0.006     1.066358    1.450827
       newrevwaveny |   2.070408    .637528     2.36   0.018     1.132268    3.785842
        politreform |   1.252343   .4943568     0.57   0.569     .5777137    2.714776
       electoralrev |   1.713322   .7032701     1.31   0.190     .7663748    3.830334
        externalwar |   1.752575   .6366983     1.54   0.122      .859871     3.57207
          startyear |   .9993501   .0061419    -0.11   0.916     .9873762    1.011469
              _cons |   .5861743   6.961908    -0.04   0.964     4.49e-11    7.66e+09
-------------------------------------------------------------------------------------

. mimrgns, atmeans at(newrevwaveny=(0 1)) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        288
                                                Average RVI       =     0.0079
                                                Largest FMI       =     0.0124
DF adjustment:   Large sample                   DF:     min       = 123,053.54
                                                        avg       = 1031884.67
Within VCE type: Delta-method                           max       = 1940715.79

Expression   : Pr(success), predict(pr)

1._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newmilexps~e    =    5.531778 (mean)
               newrevwaveny    =           0
               politreform     =    .1597222 (mean)
               electoralrev    =        .125 (mean)
               externalwar     =    .1909722 (mean)
               startyear       =    1969.438 (mean)

2._at        : newpolitym~1    =   -1.397049 (mean)
               newpolitym~q    =    37.89965 (mean)
               newincumbp~r    =    7.554514 (mean)
               newgdppcthl     =    2.719166 (mean)
               newmilexps~e    =    5.531778 (mean)
               newrevwaveny    =           1
               politreform     =    .1597222 (mean)
               electoralrev    =        .125 (mean)
               externalwar     =    .1909722 (mean)
               startyear       =    1969.438 (mean)

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .2925449   .0375512     7.79   0.000     .2189459    .3661439
          2  |    .461252   .0602563     7.65   0.000     .3431506    .5793534
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. 
. * Matching to identify the independent treatment effect on the treated for 
. *    mobilizing within a revolutionary wave
. * Using propensity score matching on complete-case sample (n=272)
. teffects psmatch (success) (newrevwaveny newpolitymin1 newpolitymin1sq  newgdppcthl   urbandum, logit ), atet nn
> (1) vce(robust)

Treatment-effects estimation                   Number of obs      =        272
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: logit                                        max =          2
------------------------------------------------------------------------------
             |              AI Robust
     success |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATET         |
newrevwaveny |
   (1 vs 0)  |   .1666667   .0799507     2.08   0.037     .0099661    .3233672
------------------------------------------------------------------------------

. * Balances checked before and after matching
. tebalance summarize
note: refitting the model using the generate() option

  Covariate balance summary
                                                   Raw      Matched
                          -----------------------------------------
                          Number of obs =          272          192
                          Treated obs   =           96           96
                          Control obs   =          176           96
                          -----------------------------------------

  -----------------------------------------------------------------
                  |Standardized differences          Variance ratio
                  |        Raw     Matched           Raw    Matched
  ----------------+------------------------------------------------
    newpolitymin1 |   -.246184    .0922208        .93064   1.148024
  newpolitymin1sq |   .0770106    .0559115      .7938582   .7976309
      newgdppcthl |   .5092342    .0915151      1.730871   .9817142
         urbandum |    .741873           0      .6410007          1
  -----------------------------------------------------------------

. * note: refitting the model using the generate() option
. *                       Raw      Matched
. * Number of obs =       272          192
. * Treated obs   =        96           96
. * Control obs   =       176          96
. *
. *                       Standardized differences                Variance ratio
. *                               Raw     Matched                 Raw       Matched       
. * newpolitymin1    -.246184     .0922208         .93064    1.148024
. * newpolitymin1sq   .0770106   .0559115         .7938582   .7976309
. * newgdppcthl      .5092342   .0915151          1.730871   .9817142
. * urbandum          .741873          0          .6410007      1
. *
. * =============================
. *  TABLE 4.1--regression table
. * =============================
. * Model 1
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace):  logit success c.newpolitymin1 newpolitymin1sq if startyear
> >1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0339
                                                Largest FMI       =     0.0718
DF adjustment:   Large sample                   DF:     min       =   3,738.70
                                                        avg       =  10,760.55
                                                        max       =  14,426.71
Model F test:       Equal FMI                   F(   2,13437.8)   =       9.09
Within VCE type:          OIM                   Prob > F          =     0.0001

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |   .8950604   .0238367    -4.16   0.000     .8495359    .9430243
newpolitymin1sq |   .9868593   .0050673    -2.58   0.010     .9769742    .9968444
          _cons |   .7930292   .1640453    -1.12   0.262     .5286829    1.189551
---------------------------------------------------------------------------------

. * Accuracy of predictions
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.6311       0.0324        0.56770     0.69457

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly: logit success c.newpolitymin1##c.newpolitymin1 if startyear>1899 & sample==1, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -143.8411       3    293.6823   304.0354
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        21            24  |         45
     -     |        67           121  |        188
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   23.86%
Specificity                     Pr( -|~D)   83.45%
Positive predictive value       Pr( D| +)   46.67%
Negative predictive value       Pr(~D| -)   64.36%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   16.55%
False - rate for true D         Pr( -| D)   76.14%
False + rate for classified +   Pr(~D| +)   53.33%
False - rate for classified -   Pr( D| -)   35.64%
--------------------------------------------------
Correctly classified                        60.94%
--------------------------------------------------

. drop sample

. 
. * Model 2
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success incgovmonarchy incgovmilitary incgovoneparty 
> incgovcompauth incgovother if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =   1.88e+62
                                                        avg       =   1.88e+62
                                                        max       =          .
Model F test:       Equal FMI                   F(   5,      .)   =       2.30
Within VCE type:          OIM                   Prob > F          =     0.0426

--------------------------------------------------------------------------------
       success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
incgovmonarchy |     4.6875   2.569995     2.82   0.005     1.600504    13.72859
incgovmilitary |   4.761905    2.46999     3.01   0.003     1.722916    13.16125
incgovoneparty |   3.648649   1.770601     2.67   0.008     1.409498    9.444948
incgovcompauth |   3.018868   1.420943     2.35   0.019     1.200036    7.594406
   incgovother |   2.352941   1.402424     1.44   0.151     .7315924    7.567509
         _cons |         .2   .0828079    -3.89   0.000     .0888381    .4502575
--------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.6118       0.0327        0.54765     0.67597

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly: logit success ib(#5).new2incumbgovtype if startyear>1899 & sample==1, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -147.7534       6    307.5068    328.213
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |         0             0  |          0
     -     |        88           145  |        233
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)    0.00%
Specificity                     Pr( -|~D)  100.00%
Positive predictive value       Pr( D| +)       .%
Negative predictive value       Pr(~D| -)   62.23%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)    0.00%
False - rate for true D         Pr( -| D)  100.00%
False + rate for classified +   Pr(~D| +)       .%
False - rate for classified -   Pr( D| -)   37.77%
--------------------------------------------------
Correctly classified                        62.23%
--------------------------------------------------

. drop sample

. 
. * Model 3
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success c.newpolitymin1 newpolitymin1sq incgovmonarch
> y incgovmilitary incgovoneparty incgovcompauth incgovother if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0180
                                                Largest FMI       =     0.0865
DF adjustment:   Large sample                   DF:     min       =   2,578.55
                                                        avg       =  45,574.14
                                                        max       = 106,128.06
Model F test:       Equal FMI                   F(   7,313170.4)  =       2.81
Within VCE type:          OIM                   Prob > F          =     0.0064

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
  newpolitymin1 |   .8973213   .0331369    -2.93   0.003     .8346571    .9646901
newpolitymin1sq |   .9866661   .0055988    -2.37   0.018     .9757483    .9977061
 incgovmonarchy |   1.490686   1.014342     0.59   0.557     .3928044    5.657126
 incgovmilitary |   1.227786   .8299883     0.30   0.761     .3263567     4.61905
 incgovoneparty |   .9314234   .6109041    -0.11   0.914     .2575398    3.368604
 incgovcompauth |     1.1294   .6418354     0.21   0.830     .3707707    3.440252
    incgovother |   .9175738   .6428353    -0.12   0.902     .2324288    3.622364
          _cons |   .7331425   .4231389    -0.54   0.591     .2365251    2.272477
---------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.6536       0.0320        0.59095     0.71631

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype if startyear>1899 & sample==1, 
> nolog or

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -141.9348       8    299.8696   327.4779
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        25            23  |         48
     -     |        63           122  |        185
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   28.41%
Specificity                     Pr( -|~D)   84.14%
Positive predictive value       Pr( D| +)   52.08%
Negative predictive value       Pr(~D| -)   65.95%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   15.86%
False - rate for true D         Pr( -| D)   71.59%
False + rate for classified +   Pr(~D| +)   47.92%
False - rate for classified -   Pr( D| -)   34.05%
--------------------------------------------------
Correctly classified                        63.09%
--------------------------------------------------

. drop sample

. 
. * Model 4
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newincumbage if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0334
                                                Largest FMI       =     0.1090
DF adjustment:   Large sample                   DF:     min       =   1,632.73
                                                        avg       = 355,404.80
                                                        max       = 1140137.79
Model F test:       Equal FMI                   F(   4,43275.1)   =       6.80
Within VCE type:          OIM                   Prob > F          =     0.0000

-----------------------------------------------------------------------------------
          success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
    newpolitymin1 |   .9057748   .0257951    -3.48   0.001     .8565954    .9577777
  newpolitymin1sq |   .9828891   .0054897    -3.09   0.002     .9721803    .9937158
newincumbpowerdur |    1.04549   .0174759     2.66   0.008     1.011792    1.080311
     newincumbage |   1.013947   .0117277     1.20   0.231     .9912197    1.037196
            _cons |   .3068681   .1961401    -1.85   0.065     .0876779    1.074022
-----------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.7044       0.0313        0.64300     0.76580

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly: logit success c.newpolitymin1##c.newpolitymin1 newincumbpowerdur newincumbage if startyear>1899 & sampl
> e==1, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -134.6781       5    279.3562   296.6114
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        37            18  |         55
     -     |        51           127  |        178
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   42.05%
Specificity                     Pr( -|~D)   87.59%
Positive predictive value       Pr( D| +)   67.27%
Negative predictive value       Pr(~D| -)   71.35%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   12.41%
False - rate for true D         Pr( -| D)   57.95%
False + rate for classified +   Pr(~D| +)   32.73%
False - rate for classified -   Pr( D| -)   28.65%
--------------------------------------------------
Correctly classified                        70.39%
--------------------------------------------------

. drop sample

. 
. * Model 5
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newlnoill newmilexpsold10tile if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0549
                                                Largest FMI       =     0.1272
DF adjustment:   Large sample                   DF:     min       =   1,200.70
                                                        avg       =   8,456.38
                                                        max       =  19,161.31
Model F test:       Equal FMI                   F(   6,32648.5)   =       6.98
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .8920419   .0272617    -3.74   0.000     .8401646    .9471225
    newpolitymin1sq |   .9806065    .005834    -3.29   0.001     .9692307    .9921158
  newincumbpowerdur |    1.05458   .0179845     3.12   0.002     1.019909    1.090428
        newgdppcthl |   .9777451   .0639303    -0.34   0.731      .860131    1.111442
          newlnoill |    .895363   .0303414    -3.26   0.001     .8378233    .9568543
newmilexpsold10tile |   1.276194    .081868     3.80   0.000      1.12527    1.447359
              _cons |   .2618776   .0944115    -3.72   0.000     .1291746    .5309083
-------------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.7696       0.0279        0.71486     0.82427

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly: logit success c.newpolitymin1##c.newpolitymin1 newincumbpowerdur newgdppcthl newlnoill newmilexpsold10t
> ile if startyear>1899 & sample==1, nolog or

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -123.8098       7    261.6195   285.7768
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        50            27  |         77
     -     |        38           118  |        156
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   56.82%
Specificity                     Pr( -|~D)   81.38%
Positive predictive value       Pr( D| +)   64.94%
Negative predictive value       Pr(~D| -)   75.64%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   18.62%
False - rate for true D         Pr( -| D)   43.18%
False + rate for classified +   Pr(~D| +)   35.06%
False - rate for classified -   Pr( D| -)   24.36%
--------------------------------------------------
Correctly classified                        72.10%
--------------------------------------------------

. drop sample

. 
. * Model 6
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0798
                                                Largest FMI       =     0.2120
DF adjustment:   Large sample                   DF:     min       =     437.43
                                                        avg       =   4,301.25
                                                        max       =  14,659.81
Model F test:       Equal FMI                   F(   8,22005.8)   =       6.05
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .8954253   .0284403    -3.48   0.001     .8413614    .9529631
    newpolitymin1sq |   .9784535   .0062559    -3.41   0.001     .9662528    .9908082
  newincumbpowerdur |   1.045536   .0182613     2.55   0.011     1.010346    1.081951
        newgdppcthl |   .8503554   .0665271    -2.07   0.038     .7294369    .9913185
          newlnoill |   .8865447   .0321929    -3.32   0.001     .8256359    .9519469
newmilexpsold10tile |   1.468702   .1358766     4.15   0.000     1.224627    1.761422
           civilwar |   1.031639   .7203229     0.04   0.964     .2618121    4.065053
      newcivxmilexp |   .7980859   .0971743    -1.85   0.065     .6282326    1.013862
              _cons |   .3501831    .162551    -2.26   0.024     .1409348     .870106
-------------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.8088       0.0264        0.75704     0.86051

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile newrevwaveny if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly: logit success c.newpolitymin1##c.newpolitymin1 newincumbpowerdur newgdppcthl newlnoill i.civilwar##c.ne
> wmilexpsold10tile newrevwaveny if startyear>1899 & sample==1, nolog or

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -116.5483      10    253.0967    287.607
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        58            20  |         78
     -     |        30           125  |        155
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   65.91%
Specificity                     Pr( -|~D)   86.21%
Positive predictive value       Pr( D| +)   74.36%
Negative predictive value       Pr(~D| -)   80.65%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.79%
False - rate for true D         Pr( -| D)   34.09%
False + rate for classified +   Pr(~D| +)   25.64%
False - rate for classified -   Pr( D| -)   19.35%
--------------------------------------------------
Correctly classified                        78.54%
--------------------------------------------------

. drop sample

. 
. * Model 7
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur revwaveny politreform electoralrev externalwar if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0184
                                                Largest FMI       =     0.0925
DF adjustment:   Large sample                   DF:     min       =   2,261.75
                                                        avg       = 3907269.90
                                                        max       =   2.20e+07
Model F test:       Equal FMI                   F(   7,300037.2)  =       4.90
Within VCE type:          OIM                   Prob > F          =     0.0000

-----------------------------------------------------------------------------------
          success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
    newpolitymin1 |   .9139564   .0264828    -3.11   0.002     .8634922    .9673699
  newpolitymin1sq |   .9830445    .005534    -3.04   0.002      .972252    .9939568
newincumbpowerdur |   1.051617   .0174424     3.03   0.002     1.017979    1.086366
        revwaveny |   1.668357    .453704     1.88   0.060      .979056    2.842958
      politreform |   1.518511   .5520136     1.15   0.251     .7447088    3.096346
     electoralrev |    1.95052   .7703911     1.69   0.091     .8994019    4.230064
      externalwar |   1.574229   .5228676     1.37   0.172     .8210091    3.018477
            _cons |   .3839224   .1059119    -3.47   0.001     .2235728    .6592771
-----------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.7309       0.0303        0.67142     0.79031

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile newrevwaveny if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly:  logit success newpolitymin1 newpolitymin1sq newincumbpowerdur revwaveny politreform electoralrev exter
> nalwar if startyear>1899 & sample==1, nolog or

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -132.7868       8    281.5737    309.182
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        41            20  |         61
     -     |        47           125  |        172
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   46.59%
Specificity                     Pr( -|~D)   86.21%
Positive predictive value       Pr( D| +)   67.21%
Negative predictive value       Pr(~D| -)   72.67%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.79%
False - rate for true D         Pr( -| D)   53.41%
False + rate for classified +   Pr(~D| +)   32.79%
False - rate for classified -   Pr( D| -)   27.33%
--------------------------------------------------
Correctly classified                        71.24%
--------------------------------------------------

. drop sample

. 
. * Model 8
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp newrevwaveny if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0708
                                                Largest FMI       =     0.2045
DF adjustment:   Large sample                   DF:     min       =     469.71
                                                        avg       =   6,673.10
                                                        max       =  29,064.82
Model F test:       Equal FMI                   F(   9,31832.7)   =       5.60
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .8993727   .0285551    -3.34   0.001     .8450918      .95714
    newpolitymin1sq |   .9787406   .0062254    -3.38   0.001     .9666015    .9910322
  newincumbpowerdur |   1.042662   .0184831     2.36   0.018     1.007054     1.07953
        newgdppcthl |   .8302218   .0669511    -2.31   0.021      .708818    .9724192
          newlnoill |   .8926493   .0327846    -3.09   0.002     .8306453    .9592817
newmilexpsold10tile |   1.470884    .136243     4.17   0.000     1.226215    1.764371
           civilwar |   1.147286   .8076768     0.20   0.845     .2879294    4.571484
      newcivxmilexp |   .7962587   .0969596    -1.87   0.062     .6268109    1.011514
       newrevwaveny |   1.582682   .5145338     1.41   0.158     .8368629     2.99318
              _cons |   .3014427    .144425    -2.50   0.012     .1178242    .7712145
-------------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(57 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.8112       0.0260        0.76017     0.86219

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. clear

. use revolutionaryeps.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile newrevwaveny if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. quietly: logit success c.newpolitymin1##c.newpolitymin1 newincumbpowerdur newgdppcthl newlnoill i.civilwar##c.ne
> wmilexpsold10tile newrevwaveny if startyear>1899 & sample==1, nolog or

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -116.5483      10    253.0967    287.607
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        58            20  |         78
     -     |        30           125  |        155
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   65.91%
Specificity                     Pr( -|~D)   86.21%
Positive predictive value       Pr( D| +)   74.36%
Negative predictive value       Pr(~D| -)   80.65%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.79%
False - rate for true D         Pr( -| D)   34.09%
False + rate for classified +   Pr(~D| +)   25.64%
False - rate for classified -   Pr( D| -)   19.35%
--------------------------------------------------
Correctly classified                        78.54%
--------------------------------------------------

. drop sample

. 
. * Model 9--complete case analysis (based on Model 6, which has lowest BIC and AIC)
. clear

. use revolutionaryepsmireg.dta

. * create comparison sample for information criteria using model with smallest sample size 
. quietly: logit success c.newpolitymin1##c.newpolitymin1 ib(#5).new2incumbgovtype newincumbage newincumbpowerdur 
> newgdppcthl newlnoill civilwar##c.newmilexpsold10tile if startyear>1899 & colony==0 , or nolog

. generate sample=e(sample)

. logit success c.newpolitymin1##c.newpolitymin1 newincumbpowerdur newgdppcthl newlnoill i.civilwar##c.newmilexpso
> ld10tile if startyear>1899 & sample==1, nolog or

Logistic regression                             Number of obs     =        233
                                                LR chi2(8)        =      75.80
                                                Prob > chi2       =     0.0000
Log likelihood = -116.56069                     Pseudo R2         =     0.2454

-------------------------------------------------------------------------------------------------
                        success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------------------+----------------------------------------------------------------
                  newpolitymin1 |   .9107321   .0313063    -2.72   0.007     .8513942    .9742056
                                |
c.newpolitymin1#c.newpolitymin1 |   .9733624   .0068914    -3.81   0.000     .9599488    .9869634
                                |
              newincumbpowerdur |   1.065094   .0214257     3.13   0.002     1.023917    1.107926
                    newgdppcthl |   .8417981   .0704414    -2.06   0.040     .7144628    .9918277
                      newlnoill |   .8638559   .0345239    -3.66   0.000     .7987724    .9342422
                                |
                       civilwar |
                           yes  |   1.072652   .8160559     0.09   0.927     .2414786    4.764737
            newmilexpsold10tile |   1.536096   .1478575     4.46   0.000     1.271996    1.855031
                                |
 civilwar#c.newmilexpsold10tile |
                           yes  |   .7894942    .101372    -1.84   0.066     .6138378    1.015417
                                |
                          _cons |   .3343205     .16716    -2.19   0.028     .1254766    .8907651
-------------------------------------------------------------------------------------------------

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        233 -154.4599  -116.5607       9    251.1214   282.1807
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * AUC
. lroc success, nograph

Logistic model for success

number of observations =      233
area under ROC curve   =   0.8197

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        57            20  |         77
     -     |        31           125  |        156
-----------+--------------------------+-----------
   Total   |        88           145  |        233

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   64.77%
Specificity                     Pr( -|~D)   86.21%
Positive predictive value       Pr( D| +)   74.03%
Negative predictive value       Pr(~D| -)   80.13%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.79%
False - rate for true D         Pr( -| D)   35.23%
False + rate for classified +   Pr(~D| +)   25.97%
False - rate for classified -   Pr( D| -)   19.87%
--------------------------------------------------
Correctly classified                        78.11%
--------------------------------------------------

. 
. * =======================================
. * FIGURE 4.6--ROC curves, by urban/rural
. * =======================================
. * Complete-case sample (based on Model 6)
. * Figure 4.6a--regime factors, by urban/rural
. clear

. use revolutionaryeps.dta

. quietly: logit success c.newpolitymin1##c.newpolitymin1 newincumbpowerdur newgdppcthl newlnoill i.civilwar##c.ne
> wmilexpsold10tile if startyear>1899 & colony==0, or nolog

. generate sample=e(sample)

. * Accuracy of model
. predict xbsucc if sample==1, xb
(111 missing values generated)

. roccomp success xbsucc if sample==1, by(urbandum) graph summary legend(position(6) order (1 2) cols(2)) ysize(6)
>  xsize(6) plotregion(lcolor(black)) ylabel(, nogrid labsize(small)) xlabel(, nogrid labsize(small)) ytick(0(.1)1
> ) xtick(0(.1)1) ylabel(0(.1)1) xlabel(0(.1)1) title({bf: 4.6A: Complete case sample} , size(medlarge)) ytitle({b
> f: True positive rate} , size(medsmall)) xtitle({bf: False positive rate} , size(medsmall)) rlopts(lpattern(dash
> )) plot1opts(msize(medlarge)) plot2opts(msize(medlarge))

                              ROC                    -Asymptotic Normal--
urbandum           Obs       Area     Std. Err.      [95% Conf. Interval]
-------------------------------------------------------------------------
0                   98     0.6830       0.0650        0.55560     0.81039
1                  136     0.8464       0.0331        0.78153     0.91118
-------------------------------------------------------------------------
Ho: area(0) = area(1)
    chi2(1) =     5.02       Prob>chi2 =   0.0251

. * Saving graph to hard drive as figure4_6a.pdf
. graph export Logfiles\figure4_6a.pdf, replace
(file Logfiles\figure4_6a.pdf written in PDF format)

. drop xbsucc

. 
. * Imputed sample (based on Model 6)
. * Figure 4.6b--regime factors, by urban/rural
. clear

. use revolutionaryepsmireg.dta

. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0798
                                                Largest FMI       =     0.2120
DF adjustment:   Large sample                   DF:     min       =     437.43
                                                        avg       =   4,301.25
                                                        max       =  14,659.81
Model F test:       Equal FMI                   F(   8,22005.8)   =       6.05
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .8954253   .0284403    -3.48   0.001     .8413614    .9529631
    newpolitymin1sq |   .9784535   .0062559    -3.41   0.001     .9662528    .9908082
  newincumbpowerdur |   1.045536   .0182613     2.55   0.011     1.010346    1.081951
        newgdppcthl |   .8503554   .0665271    -2.07   0.038     .7294369    .9913185
          newlnoill |   .8865447   .0321929    -3.32   0.001     .8256359    .9519469
newmilexpsold10tile |   1.468702   .1358766     4.15   0.000     1.224627    1.761422
           civilwar |   1.031639   .7203229     0.04   0.964     .2618121    4.065053
      newcivxmilexp |   .7980859   .0971743    -1.85   0.065     .6282326    1.013862
              _cons |   .3501831    .162551    -2.26   0.024     .1409348     .870106
-------------------------------------------------------------------------------------

. * Accuracy of model
. mi predict xbsuccmi using miest, xb
(57 missing values generated)

. roccomp success xbsuccmi, by(urbandum) graph summary legend(position(6) order (1 2) cols(2)) ysize(6) xsize(6) p
> lotregion(lcolor(black)) ylabel(, nogrid labsize(small)) xlabel(, nogrid labsize(small)) ytick(0(.1)1) xtick(0(.
> 1)1) ylabel(0(.1)1) xlabel(0(.1)1) title({bf: 4.6B: Multiple imputation (20 samples)} , size(medlarge)) ytitle({
> it: True positive rate} , size(medsmall)) xtitle({it: False positive rate} , size(medsmall)) rlopts(lpattern(das
> h)) plot1opts(msize(medlarge)) plot2opts(msize(medlarge))

                              ROC                    -Asymptotic Normal--
urbandum           Obs       Area     Std. Err.      [95% Conf. Interval]
-------------------------------------------------------------------------
0                  123     0.7513       0.0525        0.64832     0.85419
1                  165     0.8016       0.0344        0.73418     0.86900
-------------------------------------------------------------------------
Ho: area(0) = area(1)
    chi2(1) =     0.64       Prob>chi2 =   0.4227

. * Saving graph to hard drive as figure4_6a.pdf
. graph export Logfiles\figure4_6b.pdf, replace
(file Logfiles\figure4_6b.pdf written in PDF format)

. * Figures manipulated in Stata graph editor
. * Figures combined in Stata graph editor and edited.
. drop xbsuccmi

. 
. * ====================================================
. * Movement characteristics and revolutionary outcomes
. * ====================================================
. * =================================================
. * FIGURE 4.7--effect of participation on outcomes
. * =================================================
. * Using multiple imputation data for opposition tactics
. clear

. use revolutionaryepsmiopp.dta

. set seed 1234

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0171
                                                Largest FMI       =     0.0283
DF adjustment:   Large sample                   DF:     min       =  23,941.92
                                                        avg       =  24,136.32
                                                        max       =  24,330.73
Model F test:       Equal FMI                   F(   1,24330.7)   =      34.94
Within VCE type:          OIM                   Prob > F          =     0.0000

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.528654   .1097591     5.91   0.000     1.327972    1.759663
       _cons |   .0059561   .0046822    -6.52   0.000     .0012758    .0278063
------------------------------------------------------------------------------

. mimrgns, at (lnparticnum = (6.907705527 9.210340372 9.903487553 10.30895266 10.59663473 10.81977828 11.00209984 
> 11.15625052 11.28978191 11.40756495 11.51292546 11.60823564 11.69524702 11.77528973 11.8493977 11.91839057 11.98
> 292909 12.04355372 12.10071213 12.15477935 12.20607265 12.25486281 12.30138283 12.34583459 12.3883942 12.4292162
>  12.46843691 12.50617724 12.54254488 12.5776362 12.61153775 12.64432758 12.67607627 12.70684793 12.7367009 12.76
> 568843 12.79385931 12.82125828 12.84792653 12.87390202 12.89921983 12.92391244 12.94800999 12.97154049 12.994530
> 01 13.01700286 13.03898177 13.06048797 13.08154138 13.10216067 13.12236338 13.142166 13.16158409 13.18063229 13.
> 19932442 13.21767356 13.23569206 13.25339164 13.27078338 13.28787782 13.30468493)) expression(exp(predict(xb))/(
> 1+exp(predict(xb))))

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        343
                                                Average RVI       =     0.0088
                                                Largest FMI       =     0.0259
DF adjustment:   Large sample                   DF:     min       =  28,503.85
                                                        avg       = 151,306.98
Within VCE type: Delta-method                           max       = 697,581.58

Expression   : exp(predict(xb))/(1+exp(predict(xb)))

1._at        : lnparticnum     =    6.907706

2._at        : lnparticnum     =     9.21034

3._at        : lnparticnum     =    9.903488

4._at        : lnparticnum     =    10.30895

5._at        : lnparticnum     =    10.59663

6._at        : lnparticnum     =    10.81978

7._at        : lnparticnum     =     11.0021

8._at        : lnparticnum     =    11.15625

9._at        : lnparticnum     =    11.28978

10._at       : lnparticnum     =    11.40756

11._at       : lnparticnum     =    11.51293

12._at       : lnparticnum     =    11.60824

13._at       : lnparticnum     =    11.69525

14._at       : lnparticnum     =    11.77529

15._at       : lnparticnum     =     11.8494

16._at       : lnparticnum     =    11.91839

17._at       : lnparticnum     =    11.98293

18._at       : lnparticnum     =    12.04355

19._at       : lnparticnum     =    12.10071

20._at       : lnparticnum     =    12.15478

21._at       : lnparticnum     =    12.20607

22._at       : lnparticnum     =    12.25486

23._at       : lnparticnum     =    12.30138

24._at       : lnparticnum     =    12.34583

25._at       : lnparticnum     =    12.38839

26._at       : lnparticnum     =    12.42922

27._at       : lnparticnum     =    12.46844

28._at       : lnparticnum     =    12.50618

29._at       : lnparticnum     =    12.54254

30._at       : lnparticnum     =    12.57764

31._at       : lnparticnum     =    12.61154

32._at       : lnparticnum     =    12.64433

33._at       : lnparticnum     =    12.67608

34._at       : lnparticnum     =    12.70685

35._at       : lnparticnum     =     12.7367

36._at       : lnparticnum     =    12.76569

37._at       : lnparticnum     =    12.79386

38._at       : lnparticnum     =    12.82126

39._at       : lnparticnum     =    12.84793

40._at       : lnparticnum     =     12.8739

41._at       : lnparticnum     =    12.89922

42._at       : lnparticnum     =    12.92391

43._at       : lnparticnum     =    12.94801

44._at       : lnparticnum     =    12.97154

45._at       : lnparticnum     =    12.99453

46._at       : lnparticnum     =      13.017

47._at       : lnparticnum     =    13.03898

48._at       : lnparticnum     =    13.06049

49._at       : lnparticnum     =    13.08154

50._at       : lnparticnum     =    13.10216

51._at       : lnparticnum     =    13.12236

52._at       : lnparticnum     =    13.14217

53._at       : lnparticnum     =    13.16158

54._at       : lnparticnum     =    13.18063

55._at       : lnparticnum     =    13.19932

56._at       : lnparticnum     =    13.21767

57._at       : lnparticnum     =    13.23569

58._at       : lnparticnum     =    13.25339

59._at       : lnparticnum     =    13.27078

60._at       : lnparticnum     =    13.28788

61._at       : lnparticnum     =    13.30468

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1005704   .0276293     3.64   0.000     .0464156    .1547252
          2  |   .2289152   .0294431     7.77   0.000     .1712066    .2866239
          3  |   .2848836   .0279413    10.20   0.000     .2301191     .339648
          4  |   .3211887   .0274332    11.71   0.000     .2674202    .3749571
          5  |   .3483647   .0275658    12.64   0.000     .2943365     .402393
          6  |   .3701579   .0280646    13.19   0.000     .3151523    .4251636
          7  |   .3883707   .0287635    13.50   0.000     .3319952    .4447463
          8  |    .404019   .0295646    13.67   0.000     .3460733    .4619648
          9  |    .417735   .0304103    13.74   0.000     .3581319    .4773382
         10  |   .4299408   .0312663    13.75   0.000     .3686598    .4912219
         11  |   .4409323   .0321128    13.73   0.000     .3779921    .5038725
         12  |   .4509257   .0329384    13.69   0.000     .3863674     .515484
         13  |   .4600839   .0337366    13.64   0.000     .3939611    .5262066
         14  |   .4685325   .0345042    13.58   0.000     .4009051    .5361599
         15  |   .4763709   .0352401    13.52   0.000     .4073011    .5454407
         16  |   .4836788   .0359442    13.46   0.000      .413229    .5541285
         17  |   .4905212    .036617    13.40   0.000     .4187527    .5622898
         18  |   .4969518   .0372597    13.34   0.000     .4239236    .5699801
         19  |   .5030158   .0378735    13.28   0.000     .4287844    .5772471
         20  |   .5087509   .0384599    13.23   0.000     .4333702    .5841316
         21  |   .5141897   .0390202    13.18   0.000     .4377107    .5906686
         22  |   .5193599   .0395559    13.13   0.000     .4418309    .5968888
         23  |   .5242856   .0400684    13.08   0.000     .4457522     .602819
         24  |   .5289879   .0405589    13.04   0.000     .4494931    .6084827
         25  |   .5334852   .0410287    13.00   0.000     .4530696    .6139009
         26  |   .5377938    .041479    12.97   0.000     .4564955    .6190921
         27  |    .541928   .0419109    12.93   0.000     .4597831    .6240729
         28  |   .5459006   .0423254    12.90   0.000     .4629433     .628858
         29  |   .5497234   .0427236    12.87   0.000     .4659856    .6334611
         30  |   .5534062   .0431062    12.84   0.000     .4689185    .6378939
         31  |   .5569587   .0434742    12.81   0.000     .4717497    .6421677
         32  |   .5603891   .0438283    12.79   0.000      .474486    .6462922
         33  |   .5637051   .0441692    12.76   0.000     .4771336    .6502765
         34  |   .5669136   .0444978    12.74   0.000     .4796982     .654129
         35  |   .5700209   .0448145    12.72   0.000     .4821848    .6578571
         36  |   .5730329     .04512    12.70   0.000     .4845979    .6614679
         37  |   .5759548   .0454148    12.68   0.000     .4869419    .6649676
         38  |   .5787915   .0456995    12.67   0.000     .4892206    .6683624
         39  |   .5815475   .0459746    12.65   0.000     .4914374    .6716576
         40  |   .5842271   .0462405    12.63   0.000     .4935959    .6748584
         41  |    .586834   .0464976    12.62   0.000     .4956987    .6779692
         42  |   .5893718   .0467464    12.61   0.000      .497749    .6809947
         43  |    .591844   .0469872    12.60   0.000     .4997491    .6839389
         44  |   .5942534   .0472204    12.58   0.000     .5017014    .6868054
         45  |    .596603   .0474463    12.57   0.000     .5036083    .6895978
         46  |   .5988956   .0476652    12.56   0.000     .5054717    .6923195
         47  |   .6011335   .0478775    12.56   0.000     .5072935    .6949735
         48  |   .6033193   .0480834    12.55   0.000     .5090757    .6975629
         49  |    .605455   .0482832    12.54   0.000     .5108199    .7000901
         50  |   .6075428   .0484771    12.53   0.000     .5125276     .702558
         51  |   .6095845   .0486653    12.53   0.000     .5142003    .7049688
         52  |   .6115822   .0488482    12.52   0.000     .5158395    .7073248
         53  |   .6135373   .0490259    12.51   0.000     .5174465    .7096282
         54  |   .6154517   .0491985    12.51   0.000     .5190224    .7118811
         55  |   .6173268   .0493664    12.51   0.000     .5205685    .7140852
         56  |   .6191641   .0495296    12.50   0.000     .5220858    .7162424
         57  |    .620965   .0496884    12.50   0.000     .5235754    .7183545
         58  |   .6227307   .0498429    12.49   0.000     .5250383    .7204231
         59  |   .6244626   .0499933    12.49   0.000     .5264754    .7224497
         60  |   .6261617   .0501397    12.49   0.000     .5278876    .7244358
         61  |   .6278292   .0502822    12.49   0.000     .5292757    .7263827
------------------------------------------------------------------------------

. * Classificatory power of participation alone--complete case sample
. clear

. use revolutionaryeps.dta

. logit success lnparticnum if startyear>1899, or nolog

Logistic regression                             Number of obs     =        320
                                                LR chi2(1)        =      37.77
                                                Prob > chi2       =     0.0000
Log likelihood = -187.67289                     Pseudo R2         =     0.0914

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |    1.51772   .1112015     5.69   0.000     1.314695    1.752097
       _cons |   .0061637   .0049408    -6.35   0.000     .0012809    .0296591
------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        41            26  |         67
     -     |        70           183  |        253
-----------+--------------------------+-----------
   Total   |       111           209  |        320

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   36.94%
Specificity                     Pr( -|~D)   87.56%
Positive predictive value       Pr( D| +)   61.19%
Negative predictive value       Pr(~D| -)   72.33%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   12.44%
False - rate for true D         Pr( -| D)   63.06%
False + rate for classified +   Pr(~D| +)   38.81%
False - rate for classified -   Pr( D| -)   27.67%
--------------------------------------------------
Correctly classified                        70.00%
--------------------------------------------------

. 
. *  Median levels of participation, urban vs. rural revolts
. sum particnum if urbandum==1 & startyear>1899, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1500           1000
 5%         3000           1500
10%        10000           1760       Obs                 159
25%        25000           2000       Sum of Wgt.         159

50%       100000                      Mean           425338.1
                        Largest       Std. Dev.       1210041
75%       300000        2500000
90%      1000000        3000000       Variance       1.46e+12
95%      2000000       1.00e+07       Skewness       6.472594
99%     1.00e+07       1.00e+07       Kurtosis       50.26205

. sum particnum if urbandum==0 & startyear>1899, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1000           1000
 5%         1500           1000
10%         2500           1000       Obs                 161
25%         7500           1000       Sum of Wgt.         161

50%        15000                      Mean           63544.72
                        Largest       Std. Dev.      323905.9
75%        30000         350000
90%        90000         450000       Variance       1.05e+11
95%       170000         775000       Skewness        11.3403
99%       775000        4000000       Kurtosis       137.3626

. * t-tests of literacy, schooling, newspaper readership, television ownership, and mobile phone ownership in urba
> n vs. rural environments
. ttest litpercbefrev if startyear>1899, by(urbandum)

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      no |      65    34.47385    2.904216    23.41454    28.67201    40.27569
     yes |     145    63.50069    2.575273     31.0104    58.41047    68.59091
---------+--------------------------------------------------------------------
combined |     210    54.51619    2.194564    31.80225    50.18987    58.84251
---------+--------------------------------------------------------------------
    diff |           -29.02684    4.311887               -37.52745   -20.52624
------------------------------------------------------------------------------
    diff = mean(no) - mean(yes)                                   t =  -6.7318
Ho: diff = 0                                     degrees of freedom =      208

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest avgtotalyrsschool if startyear>1899, by(urbandum)

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      no |      43    2.473256    .2304184    1.510955    2.008253    2.938259
     yes |      86    5.050698    .3242326    3.006809    4.406036    5.695359
---------+--------------------------------------------------------------------
combined |     129     4.19155    .2527466     2.87065    3.691448    4.691653
---------+--------------------------------------------------------------------
    diff |           -2.577442    .4872562               -3.541634    -1.61325
------------------------------------------------------------------------------
    diff = mean(no) - mean(yes)                                   t =  -5.2897
Ho: diff = 0                                     degrees of freedom =      127

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest newspercap if startyear>1899, by(urbandum)

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      no |      42    250.3333    48.58442     314.863    152.2151    348.4516
     yes |      67    950.9104    154.4178    1263.964    642.6054    1259.215
---------+--------------------------------------------------------------------
combined |     109    680.9633    101.8757    1063.614     479.028    882.8986
---------+--------------------------------------------------------------------
    diff |           -700.5771    199.1038               -1095.277   -305.8771
------------------------------------------------------------------------------
    diff = mean(no) - mean(yes)                                   t =  -3.5187
Ho: diff = 0                                     degrees of freedom =      107

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0003         Pr(|T| > |t|) = 0.0006          Pr(T > t) = 0.9997

. ttest televispercap if startyear>1899, by(urbandum)

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      no |      45    1883.444    509.6588    3418.895    856.2946    2910.594
     yes |      67    10675.24    1839.858    15059.89    7001.843    14348.63
---------+--------------------------------------------------------------------
combined |     112    7142.821    1188.611    12579.08    4787.508    9498.134
---------+--------------------------------------------------------------------
    diff |           -8791.794    2286.643               -13323.38   -4260.205
------------------------------------------------------------------------------
    diff = mean(no) - mean(yes)                                   t =  -3.8448
Ho: diff = 0                                     degrees of freedom =      110

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0001         Pr(|T| > |t|) = 0.0002          Pr(T > t) = 0.9999

. ttest mobilepercap if startyear>1899, by(urbandum)

Two-sample t test with equal variances
------------------------------------------------------------------------------
Variable |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
      no |      15      3202.2    2763.517    10703.05   -2724.953    9129.353
     yes |      39    17904.26    4538.461    28342.68    8716.623    27091.89
---------+--------------------------------------------------------------------
combined |      54    13820.35    3470.505     25502.9      6859.4     20781.3
---------+--------------------------------------------------------------------
    diff |           -14702.06    7552.122                -29856.5    452.3841
------------------------------------------------------------------------------
    diff = mean(no) - mean(yes)                                   t =  -1.9467
Ho: diff = 0                                     degrees of freedom =       52

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0285         Pr(|T| > |t|) = 0.0570          Pr(T > t) = 0.9715

. 
. * =================================================================
. * FIGURE 4.8--effect of participation on outcomes in urban, rural, 
. *    urban civic, and social revolutionary episodes
. * =================================================================
. * Multiple imputation sample
. clear

. use revolutionaryepsmiopp.dta

. set seed 1234

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum if startyear>1899 & urbandum==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        180
                                                Average RVI       =     0.0304
                                                Largest FMI       =     0.0524
DF adjustment:   Large sample                   DF:     min       =   7,004.12
                                                        avg       =   7,049.55
                                                        max       =   7,094.99
Model F test:       Equal FMI                   F(   1, 7095.0)   =      12.37
Within VCE type:          OIM                   Prob > F          =     0.0004

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.392736   .1311799     3.52   0.000     1.157928    1.675159
       _cons |    .019147   .0208631    -3.63   0.000     .0022618    .1620892
------------------------------------------------------------------------------

. mimrgns, at (lnparticnum = (1 9.210340372 9.903487553 10.30895266 10.59663473 10.81977828 11.00209984 11.1562505
> 2 11.28978191 11.40756495 11.51292546 11.60823564 11.69524702 11.77528973 11.8493977 11.91839057 11.98292909 12.
> 04355372 12.10071213 12.15477935 12.20607265 12.25486281 12.30138283 12.34583459 12.3883942 12.4292162 12.468436
> 91 12.50617724 12.54254488 12.5776362 12.61153775 12.64432758 12.67607627 12.70684793 12.7367009 12.76568843 12.
> 79385931 12.82125828 12.84792653 12.87390202 12.89921983 12.92391244 12.94800999 12.97154049 12.99453001 13.0170
> 0286 13.03898177 13.06048797 13.08154138 13.10216067 13.12236338 13.142166 13.16158409 13.18063229 13.19932442 1
> 3.21767356 13.23569206 13.25339164 13.27078338 13.28787782 13.30468493)) expression(exp(predict(xb))/(1+exp(pred
> ict(xb))))

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        180
                                                Average RVI       =     0.0139
                                                Largest FMI       =     0.0464
DF adjustment:   Large sample                   DF:     min       =   8,925.15
                                                        avg       = 123,875.17
Within VCE type: Delta-method                           max       = 567,532.70

Expression   : exp(predict(xb))/(1+exp(predict(xb)))

1._at        : lnparticnum     =           1

2._at        : lnparticnum     =     9.21034

3._at        : lnparticnum     =    9.903488

4._at        : lnparticnum     =    10.30895

5._at        : lnparticnum     =    10.59663

6._at        : lnparticnum     =    10.81978

7._at        : lnparticnum     =     11.0021

8._at        : lnparticnum     =    11.15625

9._at        : lnparticnum     =    11.28978

10._at       : lnparticnum     =    11.40756

11._at       : lnparticnum     =    11.51293

12._at       : lnparticnum     =    11.60824

13._at       : lnparticnum     =    11.69525

14._at       : lnparticnum     =    11.77529

15._at       : lnparticnum     =     11.8494

16._at       : lnparticnum     =    11.91839

17._at       : lnparticnum     =    11.98293

18._at       : lnparticnum     =    12.04355

19._at       : lnparticnum     =    12.10071

20._at       : lnparticnum     =    12.15478

21._at       : lnparticnum     =    12.20607

22._at       : lnparticnum     =    12.25486

23._at       : lnparticnum     =    12.30138

24._at       : lnparticnum     =    12.34583

25._at       : lnparticnum     =    12.38839

26._at       : lnparticnum     =    12.42922

27._at       : lnparticnum     =    12.46844

28._at       : lnparticnum     =    12.50618

29._at       : lnparticnum     =    12.54254

30._at       : lnparticnum     =    12.57764

31._at       : lnparticnum     =    12.61154

32._at       : lnparticnum     =    12.64433

33._at       : lnparticnum     =    12.67608

34._at       : lnparticnum     =    12.70685

35._at       : lnparticnum     =     12.7367

36._at       : lnparticnum     =    12.76569

37._at       : lnparticnum     =    12.79386

38._at       : lnparticnum     =    12.82126

39._at       : lnparticnum     =    12.84793

40._at       : lnparticnum     =     12.8739

41._at       : lnparticnum     =    12.89922

42._at       : lnparticnum     =    12.92391

43._at       : lnparticnum     =    12.94801

44._at       : lnparticnum     =    12.97154

45._at       : lnparticnum     =    12.99453

46._at       : lnparticnum     =      13.017

47._at       : lnparticnum     =    13.03898

48._at       : lnparticnum     =    13.06049

49._at       : lnparticnum     =    13.08154

50._at       : lnparticnum     =    13.10216

51._at       : lnparticnum     =    13.12236

52._at       : lnparticnum     =    13.14217

53._at       : lnparticnum     =    13.16158

54._at       : lnparticnum     =    13.18063

55._at       : lnparticnum     =    13.19932

56._at       : lnparticnum     =    13.21767

57._at       : lnparticnum     =    13.23569

58._at       : lnparticnum     =    13.25339

59._at       : lnparticnum     =    13.27078

60._at       : lnparticnum     =    13.28788

61._at       : lnparticnum     =    13.30468

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0265208   .0259713     1.02   0.307     -.024389    .0774305
          2  |   .2882361   .0538017     5.36   0.000     .1827761     .393696
          3  |   .3374538   .0477439     7.07   0.000     .2438716    .4310361
          4  |    .368083   .0441156     8.34   0.000     .2816145    .4545514
          5  |   .3905032   .0418398     9.33   0.000     .3084967    .4725097
          6  |   .4082266   .0404069    10.10   0.000     .3290295    .4874237
          7  |   .4228908   .0395342    10.70   0.000     .3454046    .5003771
          8  |   .4353983   .0390485    11.15   0.000     .3588643    .5119323
          9  |   .4463006   .0388361    11.49   0.000      .370183    .5224183
         10  |    .455961   .0388197    11.75   0.000     .3798756    .5320464
         11  |    .464631   .0389449    11.93   0.000     .3883003    .5409617
         12  |   .4724927   .0391727    12.06   0.000     .3957155    .5492699
         13  |    .479682   .0394748    12.15   0.000     .4023126    .5570513
         14  |    .486303   .0398304    12.21   0.000     .4082366    .5643693
         15  |   .4924374    .040224    12.24   0.000     .4135995    .5712753
         16  |   .4981504   .0406441    12.26   0.000     .4184891    .5778117
         17  |    .503495   .0410819    12.26   0.000     .4229756    .5840145
         18  |   .5085147   .0415309    12.24   0.000     .4271154    .5899141
         19  |   .5132459    .041986    12.22   0.000     .4309544    .5955373
         20  |   .5177189   .0424434    12.20   0.000     .4345308     .600907
         21  |   .5219598   .0429003    12.17   0.000     .4378761    .6060435
         22  |   .5259907   .0433545    12.13   0.000     .4410167    .6109647
         23  |   .5298308   .0438044    12.10   0.000      .443975    .6156867
         24  |   .5334969   .0442487    12.06   0.000     .4467702    .6202236
         25  |   .5370035   .0446865    12.02   0.000     .4494186    .6245884
         26  |   .5403634   .0451171    11.98   0.000     .4519344    .6287925
         27  |    .543588   .0455402    11.94   0.000     .4543297    .6328464
         28  |   .5466874   .0459553    11.90   0.000     .4566153    .6367595
         29  |   .5496707   .0463623    11.86   0.000     .4588007    .6405406
         30  |   .5525457   .0467612    11.82   0.000     .4608939    .6441974
         31  |   .5553199   .0471518    11.78   0.000     .4629025    .6477374
         32  |   .5579998   .0475341    11.74   0.000     .4648328    .6511668
         33  |   .5605914   .0479084    11.70   0.000     .4666907     .654492
         34  |      .5631   .0482746    11.66   0.000     .4684814    .6577186
         35  |   .5655306    .048633    11.63   0.000     .4702095    .6608517
         36  |   .5678877   .0489836    11.59   0.000     .4718794    .6638961
         37  |   .5701754   .0493266    11.56   0.000     .4734946    .6668561
         38  |   .5723975   .0496622    11.53   0.000     .4750589    .6697361
         39  |   .5745574   .0499905    11.49   0.000     .4765751    .6725397
         40  |   .5766586   .0503118    11.46   0.000     .4780464    .6752707
         41  |   .5787038   .0506262    11.43   0.000     .4794752    .6779323
         42  |   .5806958    .050934    11.40   0.000      .480864    .6805276
         43  |   .5826374   .0512352    11.37   0.000      .482215    .6830597
         44  |   .5845307   .0515301    11.34   0.000     .4835302    .6855311
         45  |    .586378   .0518189    11.32   0.000     .4848115    .6879445
         46  |   .5881815   .0521017    11.29   0.000     .4860606    .6903024
         47  |   .5899429   .0523787    11.26   0.000      .487279    .6926068
         48  |   .5916643     .05265    11.24   0.000     .4884685    .6948601
         49  |   .5933472   .0529159    11.21   0.000     .4896302    .6970642
         50  |   .5949932   .0531764    11.19   0.000     .4907655     .699221
         51  |   .5966039   .0534318    11.17   0.000     .4918755    .7013323
         52  |   .5981807   .0536821    11.14   0.000     .4929615    .7033999
         53  |   .5997248   .0539276    11.12   0.000     .4940244    .7054253
         54  |   .6012377   .0541683    11.10   0.000     .4950653      .70741
         55  |   .6027203   .0544045    11.08   0.000      .496085    .7093555
         56  |   .6041738   .0546361    11.06   0.000     .4970844    .7112632
         57  |   .6055993   .0548634    11.04   0.000     .4980643    .7131343
         58  |   .6069978   .0550865    11.02   0.000     .4990255    .7149701
         59  |   .6083703   .0553055    11.00   0.000     .4999688    .7167719
         60  |   .6097177   .0555204    10.98   0.000     .5008947    .7185407
         61  |   .6110407   .0557315    10.96   0.000     .5018039    .7202775
------------------------------------------------------------------------------

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum if startyear>1899 & urbandum==0

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        163
                                                Average RVI       =     0.0015
                                                Largest FMI       =     0.0023
DF adjustment:   Large sample                   DF:     min       = 3592598.56
                                                        avg       = 4253578.46
                                                        max       = 4914558.35
Model F test:       Equal FMI                   F(   1, 3.6e+06)  =      12.11
Within VCE type:          OIM                   Prob > F          =     0.0005

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.671912   .2469742     3.48   0.001     1.251625    2.233329
       _cons |   .0021277   .0031707    -4.13   0.000     .0001147    .0394789
------------------------------------------------------------------------------

. mimrgns, at (lnparticnum = (1 9.210340372 9.903487553 10.30895266 10.59663473 10.81977828 11.00209984 11.1562505
> 2 11.28978191 11.40756495 11.51292546 11.60823564 11.69524702 11.77528973 11.8493977 11.91839057 11.98292909 12.
> 04355372 12.10071213 12.15477935 12.20607265 12.25486281 12.30138283 12.34583459 12.3883942 12.4292162 12.468436
> 91 12.50617724 12.54254488 12.5776362 12.61153775 12.64432758 12.67607627 12.70684793 12.7367009 12.76568843 12.
> 79385931 12.82125828 12.84792653 12.87390202 12.89921983 12.92391244 12.94800999 12.97154049 12.99453001 13.0170
> 0286 13.03898177 13.06048797 13.08154138 13.10216067 13.12236338 13.142166 13.16158409 13.18063229 13.19932442 1
> 3.21767356 13.23569206 13.25339164 13.27078338 13.28787782 13.30468493)) expression(exp(predict(xb))/(1+exp(pred
> ict(xb))))

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        163
                                                Average RVI       =     0.0010
                                                Largest FMI       =     0.0030
DF adjustment:   Large sample                   DF:     min       = 2143934.03
                                                        avg       =   6.91e+07
Within VCE type: Delta-method                           max       =   4.01e+09

Expression   : exp(predict(xb))/(1+exp(predict(xb)))

1._at        : lnparticnum     =           1

2._at        : lnparticnum     =     9.21034

3._at        : lnparticnum     =    9.903488

4._at        : lnparticnum     =    10.30895

5._at        : lnparticnum     =    10.59663

6._at        : lnparticnum     =    10.81978

7._at        : lnparticnum     =     11.0021

8._at        : lnparticnum     =    11.15625

9._at        : lnparticnum     =    11.28978

10._at       : lnparticnum     =    11.40756

11._at       : lnparticnum     =    11.51293

12._at       : lnparticnum     =    11.60824

13._at       : lnparticnum     =    11.69525

14._at       : lnparticnum     =    11.77529

15._at       : lnparticnum     =     11.8494

16._at       : lnparticnum     =    11.91839

17._at       : lnparticnum     =    11.98293

18._at       : lnparticnum     =    12.04355

19._at       : lnparticnum     =    12.10071

20._at       : lnparticnum     =    12.15478

21._at       : lnparticnum     =    12.20607

22._at       : lnparticnum     =    12.25486

23._at       : lnparticnum     =    12.30138

24._at       : lnparticnum     =    12.34583

25._at       : lnparticnum     =    12.38839

26._at       : lnparticnum     =    12.42922

27._at       : lnparticnum     =    12.46844

28._at       : lnparticnum     =    12.50618

29._at       : lnparticnum     =    12.54254

30._at       : lnparticnum     =    12.57764

31._at       : lnparticnum     =    12.61154

32._at       : lnparticnum     =    12.64433

33._at       : lnparticnum     =    12.67608

34._at       : lnparticnum     =    12.70685

35._at       : lnparticnum     =     12.7367

36._at       : lnparticnum     =    12.76569

37._at       : lnparticnum     =    12.79386

38._at       : lnparticnum     =    12.82126

39._at       : lnparticnum     =    12.84793

40._at       : lnparticnum     =     12.8739

41._at       : lnparticnum     =    12.89922

42._at       : lnparticnum     =    12.92391

43._at       : lnparticnum     =    12.94801

44._at       : lnparticnum     =    12.97154

45._at       : lnparticnum     =    12.99453

46._at       : lnparticnum     =      13.017

47._at       : lnparticnum     =    13.03898

48._at       : lnparticnum     =    13.06049

49._at       : lnparticnum     =    13.08154

50._at       : lnparticnum     =    13.10216

51._at       : lnparticnum     =    13.12236

52._at       : lnparticnum     =    13.14217

53._at       : lnparticnum     =    13.16158

54._at       : lnparticnum     =    13.18063

55._at       : lnparticnum     =    13.19932

56._at       : lnparticnum     =    13.21767

57._at       : lnparticnum     =    13.23569

58._at       : lnparticnum     =    13.25339

59._at       : lnparticnum     =    13.27078

60._at       : lnparticnum     =    13.28788

61._at       : lnparticnum     =    13.30468

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0035504   .0047613     0.75   0.456    -.0057816    .0128823
          2  |   .1948335   .0349543     5.57   0.000     .1263244    .2633426
          3  |   .2568043   .0362391     7.09   0.000      .185777    .3278316
          4  |   .2985434   .0407263     7.33   0.000     .2187212    .3783655
          5  |   .3303991   .0461033     7.17   0.000     .2400382    .4207599
          6  |   .3562472   .0514147     6.93   0.000     .2554763    .4570181
          7  |   .3780151   .0563592     6.71   0.000      .267553    .4884773
          8  |   .3968152   .0608703     6.52   0.000     .2775115     .516119
          9  |   .4133526   .0649606     6.36   0.000     .2860322    .5406731
         10  |    .428105   .0686672     6.23   0.000     .2935197    .5626903
         11  |   .4414112   .0720321     6.13   0.000     .3002308    .5825917
         12  |   .4535216   .0750953     6.04   0.000     .3063374    .6007058
         13  |    .464626   .0778925     5.96   0.000     .3119595    .6172925
         14  |   .4748723   .0804547     5.90   0.000      .317184    .6325607
         15  |   .4843779   .0828088     5.85   0.000     .3220756    .6466803
         16  |   .4932377    .084978     5.80   0.000     .3266838    .6597916
         17  |   .5015294   .0869822     5.77   0.000     .3310473    .6720114
         18  |   .5093173   .0888385     5.73   0.000      .335197    .6834377
         19  |    .516656   .0905619     5.71   0.000     .3391578    .6941542
         20  |   .5235912   .0921654     5.68   0.000     .3429502    .7042322
         21  |   .5301622   .0936603     5.66   0.000     .3465912    .7137331
         22  |   .5364027   .0950565     5.64   0.000     .3500953    .7227102
         23  |   .5423424   .0963628     5.63   0.000     .3534746    .7312102
         24  |   .5480067    .097587     5.62   0.000     .3567396    .7392737
         25  |   .5534182   .0987358     5.61   0.000     .3598995     .746937
         26  |    .558597   .0998155     5.60   0.000     .3629621    .7542318
         27  |   .5635606   .1008314     5.59   0.000     .3659345    .7611867
         28  |   .5683247   .1017886     5.58   0.000     .3688226    .7678268
         29  |   .5729038   .1026914     5.58   0.000     .3716323    .7741754
         30  |   .5773102   .1035437     5.58   0.000     .3743681    .7802523
         31  |   .5815557   .1043493     5.57   0.000     .3770347    .7860767
         32  |   .5856504   .1051113     5.57   0.000     .3796359    .7916648
         33  |   .5896038   .1058327     5.57   0.000     .3821754    .7970323
         34  |   .5934246   .1065163     5.57   0.000     .3846564    .8021929
         35  |   .5971205   .1071644     5.57   0.000      .387082    .8071591
         36  |   .6006988   .1077795     5.57   0.000     .3894548    .8119428
         37  |   .6041659   .1083634     5.58   0.000     .3917775    .8165543
         38  |    .607528   .1089181     5.58   0.000     .3940523    .8210038
         39  |   .6107906   .1094455     5.58   0.000     .3962813    .8252999
         40  |   .6139591    .109947     5.58   0.000     .3984668    .8294514
         41  |   .6170379   .1104242     5.59   0.000     .4006103    .8334655
         42  |   .6200317   .1108785     5.59   0.000     .4027137    .8373498
         43  |   .6229447   .1113112     5.60   0.000     .4047786    .8411108
         44  |   .6257805   .1117234     5.60   0.000     .4068065    .8447545
         45  |   .6285427   .1121163     5.61   0.000     .4087987    .8482867
         46  |   .6312349   .1124908     5.61   0.000     .4107568     .851713
         47  |   .6338598    .112848     5.62   0.000     .4126817     .855038
         48  |   .6364208   .1131887     5.62   0.000     .4145748    .8582667
         49  |   .6389202   .1135138     5.63   0.000     .4164372    .8614033
         50  |   .6413609    .113824     5.63   0.000     .4182699     .864452
         51  |   .6437452   .1141201     5.64   0.000     .4200738    .8674166
         52  |   .6460754   .1144028     5.65   0.000       .42185    .8703009
         53  |   .6483536   .1146726     5.65   0.000     .4235993    .8731079
         54  |   .6505819   .1149303     5.66   0.000     .4253225    .8758413
         55  |   .6527621   .1151764     5.67   0.000     .4270205    .8785038
         56  |   .6548961   .1154113     5.67   0.000     .4286939    .8810982
         57  |   .6569856   .1156357     5.68   0.000     .4303436    .8836275
         58  |   .6590321     .11585     5.69   0.000     .4319702     .886094
         59  |   .6610374   .1160546     5.70   0.000     .4335744    .8885003
         60  |   .6630027     .11625     5.70   0.000     .4351568    .8908486
         61  |   .6649295   .1164365     5.71   0.000      .436718    .8931409
------------------------------------------------------------------------------

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum if startyear>1899 & urbancivic==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =         54
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =          .
                                                        avg       =          .
                                                        max       =          .
Model F test:       Equal FMI                   F(   1,      .)   =       5.79
Within VCE type:          OIM                   Prob > F          =     0.0161

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.870578   .4869525     2.41   0.016     1.123025    3.115748
       _cons |   .0008042   .0025255    -2.27   0.023     1.71e-06    .3788975
------------------------------------------------------------------------------

. mimrgns, at (lnparticnum = (1 9.210340372 9.903487553 10.30895266 10.59663473 10.81977828 11.00209984 11.1562505
> 2 11.28978191 11.40756495 11.51292546 11.60823564 11.69524702 11.77528973 11.8493977 11.91839057 11.98292909 12.
> 04355372 12.10071213 12.15477935 12.20607265 12.25486281 12.30138283 12.34583459 12.3883942 12.4292162 12.468436
> 91 12.50617724 12.54254488 12.5776362 12.61153775 12.64432758 12.67607627 12.70684793 12.7367009 12.76568843 12.
> 79385931 12.82125828 12.84792653 12.87390202 12.89921983 12.92391244 12.94800999 12.97154049 12.99453001 13.0170
> 0286 13.03898177 13.06048797 13.08154138 13.10216067 13.12236338 13.142166 13.16158409 13.18063229 13.19932442 1
> 3.21767356 13.23569206 13.25339164 13.27078338 13.28787782 13.30468493)) expression(exp(predict(xb))/(1+exp(pred
> ict(xb))))

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =         54
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =   1.19e+62
                                                        avg       =   2.46e+63
Within VCE type: Delta-method                           max       =          .

Expression   : exp(predict(xb))/(1+exp(predict(xb)))

1._at        : lnparticnum     =           1

2._at        : lnparticnum     =     9.21034

3._at        : lnparticnum     =    9.903488

4._at        : lnparticnum     =    10.30895

5._at        : lnparticnum     =    10.59663

6._at        : lnparticnum     =    10.81978

7._at        : lnparticnum     =     11.0021

8._at        : lnparticnum     =    11.15625

9._at        : lnparticnum     =    11.28978

10._at       : lnparticnum     =    11.40756

11._at       : lnparticnum     =    11.51293

12._at       : lnparticnum     =    11.60824

13._at       : lnparticnum     =    11.69525

14._at       : lnparticnum     =    11.77529

15._at       : lnparticnum     =     11.8494

16._at       : lnparticnum     =    11.91839

17._at       : lnparticnum     =    11.98293

18._at       : lnparticnum     =    12.04355

19._at       : lnparticnum     =    12.10071

20._at       : lnparticnum     =    12.15478

21._at       : lnparticnum     =    12.20607

22._at       : lnparticnum     =    12.25486

23._at       : lnparticnum     =    12.30138

24._at       : lnparticnum     =    12.34583

25._at       : lnparticnum     =    12.38839

26._at       : lnparticnum     =    12.42922

27._at       : lnparticnum     =    12.46844

28._at       : lnparticnum     =    12.50618

29._at       : lnparticnum     =    12.54254

30._at       : lnparticnum     =    12.57764

31._at       : lnparticnum     =    12.61154

32._at       : lnparticnum     =    12.64433

33._at       : lnparticnum     =    12.67608

34._at       : lnparticnum     =    12.70685

35._at       : lnparticnum     =     12.7367

36._at       : lnparticnum     =    12.76569

37._at       : lnparticnum     =    12.79386

38._at       : lnparticnum     =    12.82126

39._at       : lnparticnum     =    12.84793

40._at       : lnparticnum     =     12.8739

41._at       : lnparticnum     =    12.89922

42._at       : lnparticnum     =    12.92391

43._at       : lnparticnum     =    12.94801

44._at       : lnparticnum     =    12.97154

45._at       : lnparticnum     =    12.99453

46._at       : lnparticnum     =      13.017

47._at       : lnparticnum     =    13.03898

48._at       : lnparticnum     =    13.06049

49._at       : lnparticnum     =    13.08154

50._at       : lnparticnum     =    13.10216

51._at       : lnparticnum     =    13.12236

52._at       : lnparticnum     =    13.14217

53._at       : lnparticnum     =    13.16158

54._at       : lnparticnum     =    13.18063

55._at       : lnparticnum     =    13.19932

56._at       : lnparticnum     =    13.21767

57._at       : lnparticnum     =    13.23569

58._at       : lnparticnum     =    13.25339

59._at       : lnparticnum     =    13.27078

60._at       : lnparticnum     =    13.28788

61._at       : lnparticnum     =    13.30468

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |    .001502   .0043214     0.35   0.728    -.0069678    .0099717
          2  |   .2046053   .1281029     1.60   0.110    -.0464717    .4556823
          3  |   .2842098   .1269148     2.24   0.025     .0354613    .5329584
          4  |   .3385522   .1194663     2.83   0.005     .1044026    .5727019
          5  |   .3799896   .1114832     3.41   0.001     .1614866    .5984927
          6  |    .413419   .1041797     3.97   0.000     .2092306    .6176075
          7  |   .4413535   .0978229     4.51   0.000     .2496242    .6330828
          8  |   .4652725   .0924141     5.03   0.000     .2841443    .6464007
          9  |   .4861259   .0878741     5.53   0.000     .3138958    .6583559
         10  |   .5045626   .0841027     6.00   0.000     .3397243    .6694009
         11  |   .5210456   .0809997     6.43   0.000     .3622892    .6798021
         12  |   .5359181   .0784715     6.83   0.000     .3821167    .6897194
         13  |   .5494406   .0764343     7.19   0.000     .3996321    .6992491
         14  |   .5618165   .0748138     7.51   0.000      .415184    .7084489
         15  |   .5732069   .0735451     7.79   0.000     .4290611    .7173527
         16  |    .583742   .0725719     8.04   0.000     .4415037    .7259803
         17  |   .5935285   .0718457     8.26   0.000     .4527135    .7343434
         18  |   .6026543   .0713249     8.45   0.000       .46286    .7424485
         19  |   .6111936   .0709743     8.61   0.000     .4720865    .7503008
         20  |    .619209    .070764     8.75   0.000      .480514    .7579039
         21  |   .6267535   .0706688     8.87   0.000     .4882453    .7652618
         22  |   .6338731   .0706673     8.97   0.000     .4953677    .7723786
         23  |   .6406076   .0707419     9.06   0.000      .501956    .7792592
         24  |   .6469912   .0708777     9.13   0.000     .5080736    .7859089
         25  |   .6530545   .0710622     9.19   0.000     .5137751    .7923338
         26  |   .6588239   .0712852     9.24   0.000     .5191075    .7985402
         27  |   .6643231    .071538     9.29   0.000     .5241112    .8045349
         28  |   .6695729   .0718135     9.32   0.000      .528821    .8103247
         29  |   .6745922   .0721059     9.36   0.000     .5332673    .8159172
         30  |   .6793976   .0724102     9.38   0.000     .5374763    .8213189
         31  |   .6840044   .0727224     9.41   0.000     .5414711    .8265376
         32  |   .6884258   .0730392     9.43   0.000     .5452716      .83158
         33  |   .6926745   .0733579     9.44   0.000     .5488957    .8364532
         34  |   .6967615   .0736762     9.46   0.000     .5523589    .8411642
         35  |    .700697   .0739923     9.47   0.000     .5556747    .8457193
         36  |   .7044902   .0743048     9.48   0.000     .5588554     .850125
         37  |   .7081497   .0746125     9.49   0.000     .5619118    .8543875
         38  |   .7116832   .0749145     9.50   0.000     .5648535    .8585128
         39  |   .7150978   .0752099     9.51   0.000     .5676891    .8625065
         40  |   .7184004   .0754983     9.52   0.000     .5704265    .8663743
         41  |   .7215967   .0757791     9.52   0.000     .5730724    .8701211
         42  |   .7246926   .0760522     9.53   0.000     .5756331    .8737521
         43  |   .7276934   .0763172     9.54   0.000     .5781144    .8772723
         44  |   .7306035    .076574     9.54   0.000     .5805212    .8806858
         45  |   .7334277   .0768226     9.55   0.000     .5828583    .8839972
         46  |   .7361703   .0770628     9.55   0.000     .5851298    .8872107
         47  |   .7388348   .0772949     9.56   0.000     .5873396      .89033
         48  |   .7414253   .0775187     9.56   0.000     .5894914    .8933592
         49  |   .7439449   .0777345     9.57   0.000     .5915881    .8963016
         50  |   .7463969   .0779422     9.58   0.000      .593633    .8991608
         51  |   .7487843   .0781421     9.58   0.000     .5956286      .90194
         52  |   .7511099   .0783343     9.59   0.000     .5975775    .9046423
         53  |   .7533762   .0785189     9.59   0.000     .5994819    .9072705
         54  |    .755586   .0786962     9.60   0.000     .6013442    .9098277
         55  |   .7577413   .0788663     9.61   0.000     .6031662    .9123164
         56  |   .7598444   .0790293     9.61   0.000     .6049497    .9147391
         57  |   .7618975   .0791856     9.62   0.000     .6066966    .9170984
         58  |   .7639024   .0793351     9.63   0.000     .6084084    .9193964
         59  |   .7658612   .0794782     9.64   0.000     .6100867    .9216357
         60  |   .7677754   .0796151     9.64   0.000     .6117328     .923818
         61  |   .7696467   .0797458     9.65   0.000     .6133479    .9259455
------------------------------------------------------------------------------

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum if startyear>1899 & urbandum==1 &
>  urbancivic==0

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        126
                                                Average RVI       =     0.0370
                                                Largest FMI       =     0.0659
DF adjustment:   Large sample                   DF:     min       =   4,429.03
                                                        avg       =   4,456.99
                                                        max       =   4,484.96
Model F test:       Equal FMI                   F(   1, 4485.0)   =       4.76
Within VCE type:          OIM                   Prob > F          =     0.0292

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.254666   .1304697     2.18   0.029     1.023269     1.53839
       _cons |   .0515481   .0605665    -2.52   0.012     .0051501    .5159481
------------------------------------------------------------------------------

. mimrgns, at (lnparticnum = (1 9.210340372 9.903487553 10.30895266 10.59663473 10.81977828 11.00209984 11.1562505
> 2 11.28978191 11.40756495 11.51292546 11.60823564 11.69524702 11.77528973 11.8493977 11.91839057 11.98292909 12.
> 04355372 12.10071213 12.15477935 12.20607265 12.25486281 12.30138283 12.34583459 12.3883942 12.4292162 12.468436
> 91 12.50617724 12.54254488 12.5776362 12.61153775 12.64432758 12.67607627 12.70684793 12.7367009 12.76568843 12.
> 79385931 12.82125828 12.84792653 12.87390202 12.89921983 12.92391244 12.94800999 12.97154049 12.99453001 13.0170
> 0286 13.03898177 13.06048797 13.08154138 13.10216067 13.12236338 13.142166 13.16158409 13.18063229 13.19932442 1
> 3.21767356 13.23569206 13.25339164 13.27078338 13.28787782 13.30468493)) expression(exp(predict(xb))/(1+exp(pred
> ict(xb))))

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        126
                                                Average RVI       =     0.0161
                                                Largest FMI       =     0.0580
DF adjustment:   Large sample                   DF:     min       =   5,710.81
                                                        avg       = 169,676.23
Within VCE type: Delta-method                           max       = 1459447.94

Expression   : exp(predict(xb))/(1+exp(predict(xb)))

1._at        : lnparticnum     =           1

2._at        : lnparticnum     =     9.21034

3._at        : lnparticnum     =    9.903488

4._at        : lnparticnum     =    10.30895

5._at        : lnparticnum     =    10.59663

6._at        : lnparticnum     =    10.81978

7._at        : lnparticnum     =     11.0021

8._at        : lnparticnum     =    11.15625

9._at        : lnparticnum     =    11.28978

10._at       : lnparticnum     =    11.40756

11._at       : lnparticnum     =    11.51293

12._at       : lnparticnum     =    11.60824

13._at       : lnparticnum     =    11.69525

14._at       : lnparticnum     =    11.77529

15._at       : lnparticnum     =     11.8494

16._at       : lnparticnum     =    11.91839

17._at       : lnparticnum     =    11.98293

18._at       : lnparticnum     =    12.04355

19._at       : lnparticnum     =    12.10071

20._at       : lnparticnum     =    12.15478

21._at       : lnparticnum     =    12.20607

22._at       : lnparticnum     =    12.25486

23._at       : lnparticnum     =    12.30138

24._at       : lnparticnum     =    12.34583

25._at       : lnparticnum     =    12.38839

26._at       : lnparticnum     =    12.42922

27._at       : lnparticnum     =    12.46844

28._at       : lnparticnum     =    12.50618

29._at       : lnparticnum     =    12.54254

30._at       : lnparticnum     =    12.57764

31._at       : lnparticnum     =    12.61154

32._at       : lnparticnum     =    12.64433

33._at       : lnparticnum     =    12.67608

34._at       : lnparticnum     =    12.70685

35._at       : lnparticnum     =     12.7367

36._at       : lnparticnum     =    12.76569

37._at       : lnparticnum     =    12.79386

38._at       : lnparticnum     =    12.82126

39._at       : lnparticnum     =    12.84793

40._at       : lnparticnum     =     12.8739

41._at       : lnparticnum     =    12.89922

42._at       : lnparticnum     =    12.92391

43._at       : lnparticnum     =    12.94801

44._at       : lnparticnum     =    12.97154

45._at       : lnparticnum     =    12.99453

46._at       : lnparticnum     =      13.017

47._at       : lnparticnum     =    13.03898

48._at       : lnparticnum     =    13.06049

49._at       : lnparticnum     =    13.08154

50._at       : lnparticnum     =    13.10216

51._at       : lnparticnum     =    13.12236

52._at       : lnparticnum     =    13.14217

53._at       : lnparticnum     =    13.16158

54._at       : lnparticnum     =    13.18063

55._at       : lnparticnum     =    13.19932

56._at       : lnparticnum     =    13.21767

57._at       : lnparticnum     =    13.23569

58._at       : lnparticnum     =    13.25339

59._at       : lnparticnum     =    13.27078

60._at       : lnparticnum     =    13.28788

61._at       : lnparticnum     =    13.30468

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0624117    .063457     0.98   0.325    -.0619881    .1868114
          2  |   .2941984   .0571517     5.15   0.000       .18217    .4062267
          3  |   .3277903   .0502114     6.53   0.000     .2293722    .4262085
          4  |   .3483424   .0469482     7.42   0.000     .2563232    .4403615
          5  |   .3632878   .0453425     8.01   0.000     .2744172    .4521584
          6  |   .3750703   .0446458     8.40   0.000     .2875657     .462575
          7  |   .3848113   .0444935     8.65   0.000     .2976054    .4720171
          8  |   .3931213   .0446823     8.80   0.000     .3055455    .4806972
          9  |    .400371   .0450903     8.88   0.000     .3119957    .4887464
         10  |   .4068028   .0456403     8.91   0.000     .3173495    .4962562
         11  |   .4125838    .046282     8.91   0.000     .3218725     .503295
         12  |   .4178344    .046982     8.89   0.000     .3257512    .5099176
         13  |   .4226443   .0477173     8.86   0.000     .3291199    .5161687
         14  |   .4270821   .0484722     8.81   0.000     .3320781    .5220861
         15  |   .4312014   .0492356     8.76   0.000      .334701    .5277019
         16  |    .435045        .05     8.70   0.000     .3370463    .5330437
         17  |   .4386475   .0507599     8.64   0.000     .3391592    .5381358
         18  |   .4420374   .0515117     8.58   0.000     .3410756    .5429993
         19  |   .4452385   .0522527     8.52   0.000     .3428241    .5476528
         20  |   .4482706   .0529812     8.46   0.000     .3444282    .5521129
         21  |   .4511506    .053696     8.40   0.000      .345907    .5563943
         22  |   .4538931   .0543966     8.34   0.000     .3472763      .56051
         23  |   .4565106   .0550824     8.29   0.000     .3485493    .5644719
         24  |   .4590139   .0557533     8.23   0.000     .3497374    .5682905
         25  |   .4614126   .0564095     8.18   0.000     .3508498    .5719753
         26  |   .4637149   .0570509     8.13   0.000     .3518947    .5755351
         27  |   .4659284   .0576779     8.08   0.000      .352879    .5789777
         28  |   .4680595   .0582907     8.03   0.000     .3538088    .5823102
         29  |   .4701142   .0588898     7.98   0.000     .3546891    .5855392
         30  |   .4720976   .0594753     7.94   0.000     .3555246    .5886706
         31  |   .4740147   .0600479     7.89   0.000     .3563193      .59171
         32  |   .4758695   .0606077     7.85   0.000     .3570766    .5946624
         33  |    .477666   .0611553     7.81   0.000     .3577997    .5975324
         34  |   .4794078    .061691     7.77   0.000     .3584913    .6003244
         35  |    .481098   .0622151     7.73   0.000     .3591539    .6030422
         36  |   .4827396   .0627282     7.70   0.000     .3597897    .6056896
         37  |   .4843353   .0632305     7.66   0.000     .3604006    .6082699
         38  |   .4858875   .0637224     7.63   0.000     .3609885    .6107865
         39  |   .4873985   .0642042     7.59   0.000     .3615549    .6132421
         40  |   .4888705   .0646763     7.56   0.000     .3621013    .6156397
         41  |   .4903053   .0651391     7.53   0.000      .362629    .6179816
         42  |   .4917048   .0655927     7.50   0.000     .3631391    .6202705
         43  |   .4930708   .0660376     7.47   0.000     .3636329    .6225086
         44  |   .4944046   .0664739     7.44   0.000     .3641113    .6246978
         45  |   .4957078    .066902     7.41   0.000     .3645752    .6268403
         46  |   .4969817   .0673222     7.38   0.000     .3650254     .628938
         47  |   .4982277   .0677346     7.36   0.000     .3654628    .6309925
         48  |   .4994468   .0681396     7.33   0.000      .365888    .6330057
         49  |   .5006403   .0685373     7.30   0.000     .3663016    .6349789
         50  |   .5018091    .068928     7.28   0.000     .3667044    .6369138
         51  |   .5029542    .069312     7.26   0.000     .3670968    .6388116
         52  |   .5040767   .0696893     7.23   0.000     .3674795    .6406739
         53  |   .5051772   .0700603     7.21   0.000     .3678527    .6425017
         54  |   .5062568   .0704251     7.19   0.000     .3682172    .6442964
         55  |    .507316   .0707838     7.17   0.000     .3685731    .6460589
         56  |   .5083557   .0711367     7.15   0.000      .368921    .6477905
         57  |   .5093766   .0714839     7.13   0.000     .3692611    .6494921
         58  |   .5103794   .0718256     7.11   0.000      .369594    .6511647
         59  |   .5113646   .0721619     7.09   0.000     .3699198    .6528094
         60  |   .5123328   .0724931     7.07   0.000     .3702388    .6544268
         61  |   .5132846   .0728191     7.05   0.000     .3705514    .6560179
------------------------------------------------------------------------------

. 
. * The effect of various tactics in urban and rural settings on the 
. *    probability of success, controlling for participation
. clear

. use revolutionaryeps.dta

. logit success lnpartic demonstrations strikes riots armed if startyear>1899 & urbandum==1

Iteration 0:   log likelihood = -109.07246  
Iteration 1:   log likelihood =  -97.95021  
Iteration 2:   log likelihood = -97.836785  
Iteration 3:   log likelihood = -97.836538  
Iteration 4:   log likelihood = -97.836538  

Logistic regression                             Number of obs     =        159
                                                LR chi2(5)        =      22.47
                                                Prob > chi2       =     0.0004
Log likelihood = -97.836538                     Pseudo R2         =     0.1030

--------------------------------------------------------------------------------
       success |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnparticnum |   .2428949   .1086885     2.23   0.025     .0298693    .4559205
demonstrations |   1.495631    .555857     2.69   0.007     .4061712     2.58509
       strikes |   .2579834   .3494309     0.74   0.460    -.4268885    .9428554
         riots |  -.3061745   .3570082    -0.86   0.391    -1.005898    .3935486
         armed |   .1585681   .3954773     0.40   0.688    -.6165531    .9336893
         _cons |  -4.295795   1.351133    -3.18   0.001    -6.943968   -1.647623
--------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=1 riots=0  strikes=0 armed=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           1
               strikes         =           0
               riots           =           0
               armed           =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4909757   .0796945     6.16   0.000     .3347775     .647174
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=1  strikes=0 armed=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           0
               riots           =           1
               armed           =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1372991    .077462     1.77   0.076    -.0145237    .2891219
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=0  strikes=1 armed=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           1
               riots           =           0
               armed           =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2186163   .1071681     2.04   0.041     .0085706     .428662
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=0  strikes=0 armed=1)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           0
               riots           =           0
               armed           =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2021092   .0795065     2.54   0.011     .0462792    .3579391
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=0  strikes=0 armed=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           0
               riots           =           0
               armed           =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1777404   .0873797     2.03   0.042     .0064793    .3490015
------------------------------------------------------------------------------

. *  Civil war vs. other armed
. logit success lnpartic demonstrations strikes riots armednocivwar civilwar if startyear>1899 & urbandum==1

Iteration 0:   log likelihood = -109.07246  
Iteration 1:   log likelihood = -97.926458  
Iteration 2:   log likelihood = -97.812962  
Iteration 3:   log likelihood = -97.812717  
Iteration 4:   log likelihood = -97.812717  

Logistic regression                             Number of obs     =        159
                                                LR chi2(6)        =      22.52
                                                Prob > chi2       =     0.0010
Log likelihood = -97.812717                     Pseudo R2         =     0.1032

--------------------------------------------------------------------------------
       success |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
   lnparticnum |   .2445319   .1089291     2.24   0.025     .0310348     .458029
demonstrations |   1.471901   .5659003     2.60   0.009     .3627571    2.581046
       strikes |   .2506191   .3510397     0.71   0.475    -.4374061    .9386444
         riots |   -.310618   .3578813    -0.87   0.385    -1.012052    .3908164
 armednocivwar |   .1971509   .4331907     0.46   0.649    -.6518874    1.046189
      civilwar |   .0737533   .5541338     0.13   0.894    -1.012329    1.159836
         _cons |  -4.287119   1.349655    -3.18   0.001    -6.932394   -1.641844
--------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=1 riots=0  strikes=0 armednocivwar=0 civilwar=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           1
               strikes         =           0
               riots           =           0
               armednociv~r    =           0
               civilwar        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4918692   .0798285     6.16   0.000     .3354082    .6483302
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=1  strikes=0 armednocivwar=0 civilwar=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           0
               riots           =           1
               armednociv~r    =           0
               civilwar        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1400294   .0797639     1.76   0.079     -.016305    .2963639
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=0  strikes=1 armednocivwar=0 civilwar=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           1
               riots           =           0
               armednociv~r    =           0
               civilwar        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2220416   .1092712     2.03   0.042      .007874    .4362092
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=0  strikes=0 armednocivwar=1 civilwar=0)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           0
               riots           =           0
               armednociv~r    =           1
               civilwar        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2129429   .0966261     2.20   0.028     .0235593    .4023265
------------------------------------------------------------------------------

. margins, atmeans at(demonstrations=0 riots=0  strikes=0 armednocivwar=0 civilwar=1)

Adjusted predictions                            Number of obs     =        159
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : lnparticnum     =    11.37967 (mean)
               demonstrat~s    =           0
               strikes         =           0
               riots           =           0
               armednociv~r    =           0
               civilwar        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1929937   .0875932     2.20   0.028     .0213141    .3646732
------------------------------------------------------------------------------

. logit success demonstrations  riots civilwar if startyear>1899 & urbandum==0

Iteration 0:   log likelihood = -91.934261  
Iteration 1:   log likelihood = -88.036821  
Iteration 2:   log likelihood = -87.787278  
Iteration 3:   log likelihood = -87.786544  
Iteration 4:   log likelihood = -87.786544  

Logistic regression                             Number of obs     =        163
                                                LR chi2(3)        =       8.30
                                                Prob > chi2       =     0.0403
Log likelihood = -87.786544                     Pseudo R2         =     0.0451

--------------------------------------------------------------------------------
       success |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
demonstrations |   1.068751   .6824589     1.57   0.117    -.2688438    2.406346
         riots |   .9445275    .967396     0.98   0.329    -.9515339    2.840589
      civilwar |    1.67038   .8514788     1.96   0.050      .001512    3.339247
         _cons |  -2.761661   .8499332    -3.25   0.001      -4.4275   -1.095823
--------------------------------------------------------------------------------

. 
. * ===========================================================================
. * FIGURE 4.9--effect of number of deaths on rev outcomes in urban/rural revs
. * ===========================================================================
. clear

. use revolutionaryepsmiopp.dta

. set seed 1234

. mi estimate, post dots eform saving(miest, replace): logit success i.urbandum##c.deathtile10 if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0059
                                                Largest FMI       =     0.0086
DF adjustment:   Large sample                   DF:     min       = 258,392.89
                                                        avg       = 516,991.74
                                                        max       = 815,205.45
Model F test:       Equal FMI                   F(   3,992339.1)  =      13.08
Within VCE type:          OIM                   Prob > F          =     0.0000

----------------------------------------------------------------------------------------
               success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              urbandum |
                  yes  |   136.6561     130.19     5.16   0.000     21.12038    884.2124
           deathtile10 |   1.472991   .1636949     3.49   0.000     1.184691     1.83145
                       |
urbandum#c.deathtile10 |
                  yes  |   .5145065   .0676672    -5.05   0.000     .3975955    .6657945
                       |
                 _cons |   .0174434   .0157757    -4.48   0.000     .0029635    .1026721
----------------------------------------------------------------------------------------

. mimrgns, at(urbandum=(0 1) deathtile10=(1 2 3 4 5 6 7 8 9 10)) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        343
                                                Average RVI       =     0.0027
                                                Largest FMI       =     0.0132
DF adjustment:   Large sample                   DF:     min       = 108,692.93
                                                        avg       =   1.77e+07
Within VCE type: Delta-method                           max       =   2.76e+08

Expression   : Pr(success), predict(pr)

1._at        : urbandum        =           0
               deathtile10     =           1

2._at        : urbandum        =           0
               deathtile10     =           2

3._at        : urbandum        =           0
               deathtile10     =           3

4._at        : urbandum        =           0
               deathtile10     =           4

5._at        : urbandum        =           0
               deathtile10     =           5

6._at        : urbandum        =           0
               deathtile10     =           6

7._at        : urbandum        =           0
               deathtile10     =           7

8._at        : urbandum        =           0
               deathtile10     =           8

9._at        : urbandum        =           0
               deathtile10     =           9

10._at       : urbandum        =           0
               deathtile10     =          10

11._at       : urbandum        =           1
               deathtile10     =           1

12._at       : urbandum        =           1
               deathtile10     =           2

13._at       : urbandum        =           1
               deathtile10     =           3

14._at       : urbandum        =           1
               deathtile10     =           4

15._at       : urbandum        =           1
               deathtile10     =           5

16._at       : urbandum        =           1
               deathtile10     =           6

17._at       : urbandum        =           1
               deathtile10     =           7

18._at       : urbandum        =           1
               deathtile10     =           8

19._at       : urbandum        =           1
               deathtile10     =           9

20._at       : urbandum        =           1
               deathtile10     =          10

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0250813   .0194825     1.29   0.198    -.0131039    .0632664
          2  |   .0364972   .0242278     1.51   0.132    -.0109884    .0839828
          3  |   .0528316   .0291579     1.81   0.070    -.0043169    .1099801
          4  |    .075906   .0335853     2.26   0.024     .0100799    .1417321
          5  |   .1079175   .0365044     2.96   0.003     .0363703    .1794648
          6  |   .1512294   .0369896     4.09   0.000     .0787312    .2237276
          7  |   .2078839   .0357053     5.82   0.000     .1379028    .2778651
          8  |   .2787998   .0379848     7.34   0.000     .2043509    .3532487
          9  |   .3628289   .0512045     7.09   0.000     .2624697    .4631881
         10  |   .4561577   .0732179     6.23   0.000     .3126528    .5996626
         11  |   .6436682   .0555104    11.60   0.000     .5348687    .7524678
         12  |   .5778957   .0473662    12.20   0.000      .485059    .6707324
         13  |   .5092276   .0408423    12.47   0.000     .4291781    .5892771
         14  |   .4402037   .0394178    11.17   0.000     .3629463    .5174612
         15  |    .373417   .0433616     8.61   0.000     .2884299    .4584042
         16  |   .3111363   .0493343     6.31   0.000     .2144428    .4078298
         17  |   .2550235   .0542373     4.70   0.000     .1487201    .3613268
         18  |   .2060135   .0566398     3.64   0.000     .0950012    .3170258
         19  |   .1643536   .0563283     2.92   0.004     .0539516    .2747556
         20  |   .1297496   .0537328     2.41   0.016     .0244346    .2350646
------------------------------------------------------------------------------

. 
. * ============================================================
. * Contextual nature of the advantage of non-violent action in 
. *   urban/rural episodes--beyond participation
. * ============================================================
. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum lndeaths if startyear>1899 & urba
> ndum==0

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        149
                                                Average RVI       =     0.0004
                                                Largest FMI       =     0.0010
DF adjustment:   Large sample                   DF:     min       =   1.78e+07
                                                        avg       =   7.76e+07
                                                        max       =   1.17e+08
Model F test:       Equal FMI                   F(   2, 1.1e+08)  =       7.62
Within VCE type:          OIM                   Prob > F          =     0.0005

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.279675   .2255481     1.40   0.162     .9058826    1.807705
    lndeaths |   1.301573   .1426259     2.41   0.016     1.050012    1.613403
       _cons |    .002354    .003634    -3.92   0.000     .0001142     .048514
------------------------------------------------------------------------------

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum lndeaths if startyear>1899 & urba
> ndum==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        176
                                                Average RVI       =     0.0210
                                                Largest FMI       =     0.0483
DF adjustment:   Large sample                   DF:     min       =   8,215.96
                                                        avg       = 132,016.91
                                                        max       = 379,604.08
Model F test:       Equal FMI                   F(   2,39004.1)   =      11.26
Within VCE type:          OIM                   Prob > F          =     0.0000

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.403476    .141587     3.36   0.001      1.15165    1.710368
    lndeaths |   .8286552   .0414408    -3.76   0.000     .7512861    .9139919
       _cons |   .0451286     .05261    -2.66   0.008     .0045919     .443517
------------------------------------------------------------------------------

. 
. * ===============
. * Movement goals
. * ===============
. clear

. use revolutionaryeps.dta

. logit success democrat leftist independ antimonarch ethnicorder islamist if startyear>1899, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(6)        =      47.78
                                                Prob > chi2       =     0.0000
Log likelihood =  -199.9555                     Pseudo R2         =     0.1067

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    democrat |   3.599876   1.157272     3.98   0.000      1.91711    6.759711
     leftist |   .8109078   .2583955    -0.66   0.511     .4342465    1.514281
    independ |   .6440757   .2036165    -1.39   0.164     .3466093    1.196833
 antimonarch |   2.879657   1.240479     2.46   0.014     1.237844    6.699087
 ethnicorder |   1.670605   .5693819     1.51   0.132     .8565724    3.258243
    islamist |     .47678   .2527444    -1.40   0.162     .1686903    1.347553
       _cons |   .4113077   .1130102    -3.23   0.001     .2400448    .7047603
------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        60            39  |         99
     -     |        63           181  |        244
-----------+--------------------------+-----------
   Total   |       123           220  |        343

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   48.78%
Specificity                     Pr( -|~D)   82.27%
Positive predictive value       Pr( D| +)   60.61%
Negative predictive value       Pr(~D| -)   74.18%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   17.73%
False - rate for true D         Pr( -| D)   51.22%
False + rate for classified +   Pr(~D| +)   39.39%
False - rate for classified -   Pr( D| -)   25.82%
--------------------------------------------------
Correctly classified                        70.26%
--------------------------------------------------

. margins, at (democrat=1 leftist=0 antimonarch=0 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : democrat        =           1
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .596881   .0551412    10.82   0.000     .4888062    .7049558
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=1 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           1
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5422137   .1022327     5.30   0.000     .3418413    .7425861
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=1 antimonarch=0 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : democrat        =           0
               leftist         =           1
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2501121   .0540656     4.63   0.000     .1441454    .3560788
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=1 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           1
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .209432   .0408255     5.13   0.000     .1294154    .2894485
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=0 ethnicorder=1 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           1
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4072784   .0738095     5.52   0.000     .2626145    .5519423
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=0 ethnicorder=0 islamist=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1639518   .0740575     2.21   0.027     .0188017    .3091019
------------------------------------------------------------------------------

. * Relationship between levels of participation and movement goals
. reg lnparticnum democrat leftist independ antimonarch ethnicorder islamist if startyear>1899, vce(robust)

Linear regression                               Number of obs     =        320
                                                F(6, 313)         =      13.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1879
                                                Root MSE          =     1.6809

------------------------------------------------------------------------------
             |               Robust
 lnparticnum |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    democrat |    1.39247    .249104     5.59   0.000     .9023398      1.8826
     leftist |  -.2473132   .2552655    -0.97   0.333    -.7495664      .25494
    independ |  -.4276532    .232732    -1.84   0.067    -.8855701    .0302637
 antimonarch |   .7914982   .3993824     1.98   0.048     .0056846    1.577312
 ethnicorder |   -.794449   .2266262    -3.51   0.001    -1.240352   -.3485457
    islamist |  -.3950841   .3365231    -1.17   0.241    -1.057217    .2670493
       _cons |   10.49017   .2101507    49.92   0.000     10.07668    10.90365
------------------------------------------------------------------------------

. * Median peak participation by goals
. sum particnum if startyear>1899 & democrat==1, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1000           1000
 5%        10000          10000
10%        16000          10000       Obs                  77
25%        30000          10000       Sum of Wgt.          77

50%       100000                      Mean           466194.8
                        Largest       Std. Dev.       1218617
75%       400000        2000000
90%      1000000        2000000       Variance       1.49e+12
95%      2000000        2500000       Skewness       6.473363
99%     1.00e+07       1.00e+07       Kurtosis       50.16268

. sum particnum if startyear>1899 & antimonarch==1, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1800           1800
 5%         3000           3000
10%         4000           4000       Obs                  25
25%        20000           8000       Sum of Wgt.          25

50%       100000                      Mean             298152
                        Largest       Std. Dev.      556132.3
75%       290000         500000
90%      1000000        1000000       Variance       3.09e+11
95%      2000000        2000000       Skewness       2.453134
99%      2000000        2000000       Kurtosis       7.758752

. sum particnum if startyear>1899 & leftist==1, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1000           1000
 5%         1500           1000
10%         2000           1000       Obs                  75
25%         8000           1500       Sum of Wgt.          75

50%        20000                      Mean           335662.7
                        Largest       Std. Dev.       1301856
75%       100000        2000000
90%       300000        3000000       Variance       1.69e+12
95%      2000000        4000000       Skewness       6.031034
99%     1.00e+07       1.00e+07       Kurtosis       42.77024

. sum particnum if startyear>1899 & independ==1, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1000           1000
 5%         1760           1000
10%         3000           1000       Obs                 116
25%         8500           1500       Sum of Wgt.         116

50%        20000                      Mean           114144.5
                        Largest       Std. Dev.      311338.7
75%        45000        1000000
90%       300000        1000000       Variance       9.69e+10
95%       600000        2000000       Skewness       4.539991
99%      2000000        2000000       Kurtosis       25.44768

. sum particnum if startyear>1899 & ethnicorder==1, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1000           1000
 5%         2000           1500
10%         3000           2000       Obs                  53
25%        10000           2400       Sum of Wgt.          53

50%        20000                      Mean           52875.47
                        Largest       Std. Dev.      87872.89
75%        50000         200000
90%       150000         200000       Variance       7.72e+09
95%       200000         300000       Skewness       3.231898
99%       500000         500000       Kurtosis       14.90064

. sum particnum if startyear>1899 & islamist==1, detail

         Peak number of civilian rebel participants
                  (estimated from sources)
-------------------------------------------------------------
      Percentiles      Smallest
 1%         1000           1000
 5%         1500           1500
10%         1500           1500       Obs                  29
25%         9000           1500       Sum of Wgt.          29

50%        15000                      Mean             108931
                        Largest       Std. Dev.      368909.1
75%        40000         150000
90%       200000         200000       Variance       1.36e+11
95%       250000         250000       Skewness       4.884126
99%      2000000        2000000       Kurtosis       25.54559

. * Effect of movement ideology and goals, independent of participation effects
. clear

. use revolutionaryepsmiopp.dta

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum democrat leftist independ antimon
> arch ethnicorder islamist if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0079
                                                Largest FMI       =     0.0256
DF adjustment:   Large sample                   DF:     min       =  29,108.61
                                                        avg       = 2296222.67
                                                        max       = 9156405.47
Model F test:       Equal FMI                   F(   7, 1.8e+06)  =       7.78
Within VCE type:          OIM                   Prob > F          =     0.0000

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.420076   .1120804     4.44   0.000     1.216542    1.657662
    democrat |   2.418989   .8252881     2.59   0.010     1.239447    4.721064
     leftist |    .850133   .2825529    -0.49   0.625     .4431785    1.630779
    independ |   .7326268   .2420103    -0.94   0.346     .3834452    1.399788
 antimonarch |   2.332727   1.067303     1.85   0.064     .9514915    5.719037
 ethnicorder |   2.326527   .8350692     2.35   0.019     1.151285    4.701468
    islamist |   .4885454   .2733452    -1.28   0.200     .1631739    1.462714
       _cons |   .0097498   .0087799    -5.14   0.000      .001669    .0569571
------------------------------------------------------------------------------

. mi estimate, post dots eform saving(miest, replace): logit success  democrat leftist independ antimonarch ethnic
> order islamist if startyear>1899 & urbandum==1

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        180
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =   5.93e+63
                                                        avg       =   5.93e+63
                                                        max       =          .
Model F test:       Equal FMI                   F(   6, 8.5e+65)  =       4.25
Within VCE type:          OIM                   Prob > F          =     0.0003

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    democrat |   3.220476   1.302252     2.89   0.004     1.457884    7.114057
     leftist |   .4114269    .201873    -1.81   0.070     .1572669    1.076336
    independ |   .7145469   .3425877    -0.70   0.483     .2792057    1.828678
 antimonarch |   3.813495   2.062715     2.47   0.013     1.321019    11.00873
 ethnicorder |   .5740852   .3050864    -1.04   0.296     .2025918    1.626787
    islamist |   .2815138   .3165522    -1.13   0.260     .0310709    2.550617
       _cons |   .5609988    .208133    -1.56   0.119     .2711206    1.160811
------------------------------------------------------------------------------

. mi estimate, post dots eform saving(miest, replace): logit success  democrat leftist independ antimonarch ethnic
> order islamist if startyear>1899 & urbandum==0

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        163
                                                Average RVI       =     0.0000
                                                Largest FMI       =     0.0000
DF adjustment:   Large sample                   DF:     min       =   3.26e+62
                                                        avg       =   3.26e+62
                                                        max       =          .
Model F test:       Equal FMI                   F(   6,      .)   =       2.98
Within VCE type:          OIM                   Prob > F          =     0.0066

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    democrat |   2.459809   2.790472     0.79   0.428     .2662418     22.7262
     leftist |   2.170237   1.008431     1.67   0.095     .8729398    5.395477
    independ |   .7900793   .3693447    -0.50   0.614     .3160477    1.975098
 antimonarch |   1.436049   1.310098     0.40   0.692     .2402279    8.584498
 ethnicorder |   5.111418   2.661217     3.13   0.002     1.842343    14.18118
    islamist |   1.091585   .7060212     0.14   0.892     .3072649     3.87795
       _cons |   .1798158    .085436    -3.61   0.000     .0708591    .4563098
------------------------------------------------------------------------------

. * Urban location of episodes by type of goals
. clear

. use revolutionaryeps.dta

. logit urbandum democrat leftist independ antimonarch ethnicorder islamist if startyear>1899, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(6)        =     145.56
                                                Prob > chi2       =     0.0000
Log likelihood =  -164.5487                     Pseudo R2         =     0.3067

------------------------------------------------------------------------------
    urbandum | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    democrat |    22.9137    12.8753     5.57   0.000     7.617312    68.92688
     leftist |   .6348386    .207161    -1.39   0.164     .3348873     1.20345
    independ |   .2618827   .0831878    -4.22   0.000     .1405142    .4880827
 antimonarch |   3.217945   1.582207     2.38   0.017     1.227602    8.435279
 ethnicorder |   .2113758   .0875502    -3.75   0.000      .093863    .4760103
    islamist |   .3761769   .1866334    -1.97   0.049     .1422604    .9947189
       _cons |   1.542372   .4351615     1.54   0.125     .8872236    2.681299
------------------------------------------------------------------------------

. margins, at (democrat=1 leftist=0 antimonarch=0 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(urbandum), predict()
at           : democrat        =           1
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .9724832   .0148582    65.45   0.000     .9433618    1.001605
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=1 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(urbandum), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           1
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .8323067   .0696862    11.94   0.000     .6957244    .9688891
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=1 antimonarch=0 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(urbandum), predict()
at           : democrat        =           0
               leftist         =           1
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .4947345   .0667512     7.41   0.000     .3639046    .6255644
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=1 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(urbandum), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           1
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .287709   .0472481     6.09   0.000     .1951045    .3803135
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=0 ethnicorder=1 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(urbandum), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           1
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2458637     .06722     3.66   0.000     .1141149    .3776125
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=0 ethnicorder=0 islamist=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(urbandum), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3671707   .1143876     3.21   0.001     .1429752    .5913662
------------------------------------------------------------------------------

. * Likelihood of civil war by type of goals
. logit civilwar democrat leftist independ antimonarch ethnicorder islamist if startyear>1899, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(6)        =     136.93
                                                Prob > chi2       =     0.0000
Log likelihood = -169.24876                     Pseudo R2         =     0.2880

------------------------------------------------------------------------------
    civilwar | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    democrat |   .1128668   .0478065    -5.15   0.000     .0492069    .2588843
     leftist |   2.278026   .7611438     2.46   0.014     1.183455    4.384962
    independ |   3.402703   1.081328     3.85   0.000     1.825261    6.343415
 antimonarch |    .205108   .1072988    -3.03   0.002     .0735684    .5718396
 ethnicorder |    10.1061   4.399754     5.31   0.000     4.305343    23.72245
    islamist |   3.212744   1.640612     2.29   0.022     1.180874    8.740747
       _cons |   .6128302    .173003    -1.73   0.083      .352407    1.065702
------------------------------------------------------------------------------

. margins, at (democrat=1 leftist=0 antimonarch=0 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(civilwar), predict()
at           : democrat        =           1
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .0646934   .0241415     2.68   0.007      .017377    .1120099
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=1 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(civilwar), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           1
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .111661   .0537689     2.08   0.038     .0062759    .2170462
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=1 antimonarch=0 independ=0 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(civilwar), predict()
at           : democrat        =           0
               leftist         =           1
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .5826453     .06599     8.83   0.000     .4533073    .7119832
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=1 ethnicorder=0 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(civilwar), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           1
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .6758802   .0492747    13.72   0.000     .5793036    .7724568
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=0 ethnicorder=1 islamist=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(civilwar), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           1
               islamist        =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .8609822   .0492321    17.49   0.000     .7644892    .9574753
------------------------------------------------------------------------------

. margins, at (democrat=0 leftist=0 antimonarch=0 independ=0 ethnicorder=0 islamist=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(civilwar), predict()
at           : democrat        =           0
               leftist         =           0
               independ        =           0
               antimonarch     =           0
               ethnicorder     =           0
               islamist        =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .6631711   .1130022     5.87   0.000      .441691    .8846513
------------------------------------------------------------------------------

. 
. * =====================================
. * Forms of organization and leadership
. * =====================================
. logit success  coalitionleadership vanguard traditionalleadership paramilorg underground libmovement if startyea
> r>1899, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(6)        =      34.24
                                                Prob > chi2       =     0.0000
Log likelihood = -206.72513                     Pseudo R2         =     0.0765

---------------------------------------------------------------------------------------
              success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
  coalitionleadership |   3.222529   1.047407     3.60   0.000     1.704252    6.093402
             vanguard |   .6063307   .2538876    -1.19   0.232     .2668626    1.377626
traditionalleadership |   .3545357   .1617205    -2.27   0.023     .1450063    .8668287
           paramilorg |   .7683418   .2740838    -0.74   0.460     .3818676    1.545952
          underground |   .5965683   .2656105    -1.16   0.246     .2492752    1.427714
          libmovement |   1.177278    .407669     0.47   0.637     .5972061     2.32078
                _cons |    .540085   .1208641    -2.75   0.006     .3483166    .8374332
---------------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        42            25  |         67
     -     |        81           195  |        276
-----------+--------------------------+-----------
   Total   |       123           220  |        343

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   34.15%
Specificity                     Pr( -|~D)   88.64%
Positive predictive value       Pr( D| +)   62.69%
Negative predictive value       Pr(~D| -)   70.65%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   11.36%
False - rate for true D         Pr( -| D)   65.85%
False + rate for classified +   Pr(~D| +)   37.31%
False - rate for classified -   Pr( D| -)   29.35%
--------------------------------------------------
Correctly classified                        69.10%
--------------------------------------------------

. margins, at(coalitionleadership=1 vanguard=0 traditionalleadership=0 paramil=0 underground=0 libmovement=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : coalitionl~p    =           1
               vanguard        =           0
               traditiona~p    =           0
               paramilorg      =           0
               underground     =           0
               libmovement     =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |    .635095   .0581315    10.93   0.000     .5211593    .7490307
------------------------------------------------------------------------------

. margins, at(coalitionleadership=0 vanguard=0 traditionalleadership=1 paramil=0 underground=0 libmovement=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : coalitionl~p    =           0
               vanguard        =           0
               traditiona~p    =           1
               paramilorg      =           0
               underground     =           0
               libmovement     =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .1607073   .0568265     2.83   0.005     .0493295    .2720851
------------------------------------------------------------------------------

. margins, at(coalitionleadership=0 vanguard=1 traditionalleadership=0 paramil=0 underground=0 libmovement=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : coalitionl~p    =           0
               vanguard        =           1
               traditiona~p    =           0
               paramilorg      =           0
               underground     =           0
               libmovement     =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2466874   .0677109     3.64   0.000     .1139765    .3793983
------------------------------------------------------------------------------

. margins, at(coalitionleadership=0 vanguard=0 traditionalleadership=0 paramil=0 underground=1 libmovement=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : coalitionl~p    =           0
               vanguard        =           0
               traditiona~p    =           0
               paramilorg      =           0
               underground     =           1
               libmovement     =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2436834   .0762457     3.20   0.001     .0942445    .3931222
------------------------------------------------------------------------------

. margins, at(coalitionleadership=0 vanguard=0 traditionalleadership=0 paramil=1 underground=0 libmovement=0)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : coalitionl~p    =           0
               vanguard        =           0
               traditiona~p    =           0
               paramilorg      =           1
               underground     =           0
               libmovement     =           0

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .2932712   .0637281     4.60   0.000     .1683664     .418176
------------------------------------------------------------------------------

. margins, at(coalitionleadership=0 vanguard=0 traditionalleadership=0 paramil=0 underground=0 libmovement=1)

Adjusted predictions                            Number of obs     =        343
Model VCE    : OIM

Expression   : Pr(success), predict()
at           : coalitionl~p    =           0
               vanguard        =           0
               traditiona~p    =           0
               paramilorg      =           0
               underground     =           0
               libmovement     =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |   .3886897   .0678677     5.73   0.000     .2556714    .5217079
------------------------------------------------------------------------------

. tab coalitionleadership urbandum if startyear>1899, row all

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

Leadership |
  consists |
        of |
 coalition |
        of |
  parties, |
movements, |   Episode occurred
     civil | primarily in an urban
   society |        setting
   groups? |        no        yes |     Total
-----------+----------------------+----------
        no |       156        118 |       274 
           |     56.93      43.07 |    100.00 
-----------+----------------------+----------
       yes |         7         62 |        69 
           |     10.14      89.86 |    100.00 
-----------+----------------------+----------
     Total |       163        180 |       343 
           |     47.52      52.48 |    100.00 

          Pearson chi2(1) =  48.3869   Pr = 0.000
 likelihood-ratio chi2(1) =  54.7992   Pr = 0.000
               Cramér's V =   0.3756
                    gamma =   0.8426  ASE = 0.060
          Kendall's tau-b =   0.3756  ASE = 0.041

. count if coalitionleadership==1
  69

. count if coalitionleadership==1 & (democrat==1 | antimonarch==1)
  52

. *  Independent effect of organizational forms aside from participation effects
. clear

. use revolutionaryepsmiopp.dta

. set seed 1234

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum coalitionleadership vanguard trad
> itionalleadership paramilorg underground libmovement if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0074
                                                Largest FMI       =     0.0343
DF adjustment:   Large sample                   DF:     min       =  16,288.01
                                                        avg       = 1106208.37
                                                        max       = 3214864.90
Model F test:       Equal FMI                   F(   7, 2.1e+06)  =       6.74
Within VCE type:          OIM                   Prob > F          =     0.0000

---------------------------------------------------------------------------------------
              success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
          lnparticnum |   1.450628   .1144868     4.71   0.000     1.242717    1.693323
  coalitionleadership |    2.44259   .8307592     2.63   0.009     1.254134    4.757266
             vanguard |   .6554839   .2877257    -0.96   0.336     .2772845    1.549524
traditionalleadership |   .4587412   .2146781    -1.67   0.096      .183328    1.147907
           paramilorg |   1.151311   .4344132     0.37   0.709     .5495604    2.411957
          underground |   .8215707   .3795474    -0.43   0.671     .3322068    2.031802
          libmovement |   1.580702    .572748     1.26   0.206     .7770115    3.215679
                _cons |   .0089492   .0081202    -5.20   0.000     .0015114    .0529892
---------------------------------------------------------------------------------------

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum coalitionleadership  if startyear
> >1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0123
                                                Largest FMI       =     0.0266
DF adjustment:   Large sample                   DF:     min       =  26,965.31
                                                        avg       =  96,746.00
                                                        max       = 234,605.13
Model F test:       Equal FMI                   F(   2,115907.8)  =      21.56
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
        lnparticnum |   1.433986   .1066096     4.85   0.000     1.239537    1.658938
coalitionleadership |   2.544758   .7679993     3.09   0.002     1.408496    4.597667
              _cons |   .0096059   .0076797    -5.81   0.000     .0020045    .0460345
-------------------------------------------------------------------------------------

. mimrgns, atmeans at(coalitionleadership=(0 1)) predict(pr)

Multiple-imputation estimates                   Imputations       =         20
Adjusted predictions                            Number of obs     =        343
                                                Average RVI       =     0.0049
                                                Largest FMI       =     0.0085
DF adjustment:   Large sample                   DF:     min       = 261,988.30
                                                        avg       = 7091398.07
Within VCE type: Delta-method                           max       =   1.39e+07

Expression   : Pr(success), predict(pr)

1._at        : lnparticnum     =    10.54071 (mean)
               coalitionl~p    =           0

2._at        : lnparticnum     =    10.54071 (mean)
               coalitionl~p    =           1

------------------------------------------------------------------------------
             |     Margin   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .3003059    .029187    10.29   0.000     .2431005    .3575114
          2  |   .5220303   .0670168     7.79   0.000     .3906791    .6533815
------------------------------------------------------------------------------
note: values in at() vary across imputations;
      reported values are mi point estimates.

. * Goals of liberation movements
. clear

. use revolutionaryeps.dta

. tab libmovement democrat if startyear>1899, row chi

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |     Goal: liberal
Leadership |   (civil/political
         : |      liberties,
  National | democratization, curb
liberation |    predatory govt)
  movement |        no        yes |     Total
-----------+----------------------+----------
        no |       204         85 |       289 
           |     70.59      29.41 |    100.00 
-----------+----------------------+----------
       yes |        52          2 |        54 
           |     96.30       3.70 |    100.00 
-----------+----------------------+----------
     Total |       256         87 |       343 
           |     74.64      25.36 |    100.00 

          Pearson chi2(1) =  15.8842   Pr = 0.000

. tab libmovement antimonarch if startyear>1899, row chi

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

Leadership | Goal: constitutional
         : |     (republican,
  National |   anti-monarchical,
liberation |    constitutional)
  movement |        no        yes |     Total
-----------+----------------------+----------
        no |       262         27 |       289 
           |     90.66       9.34 |    100.00 
-----------+----------------------+----------
       yes |        53          1 |        54 
           |     98.15       1.85 |    100.00 
-----------+----------------------+----------
     Total |       315         28 |       343 
           |     91.84       8.16 |    100.00 

          Pearson chi2(1) =   3.4054   Pr = 0.065

. tab libmovement leftist if startyear>1899, row chi

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

Leadership |     Goal: social
         : | revolutionary (aimed
  National | at transformation of
liberation |   class structure)
  movement |        no        yes |     Total
-----------+----------------------+----------
        no |       221         68 |       289 
           |     76.47      23.53 |    100.00 
-----------+----------------------+----------
       yes |        42         12 |        54 
           |     77.78      22.22 |    100.00 
-----------+----------------------+----------
     Total |       263         80 |       343 
           |     76.68      23.32 |    100.00 

          Pearson chi2(1) =   0.0435   Pr = 0.835

. tab libmovement independ if startyear>1899, row chi

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |     Goal: attain
Leadership | independent statehood
         : |    (includes both
  National |   anti-colonial and
liberation |      separatist)
  movement |        no        yes |     Total
-----------+----------------------+----------
        no |       210         79 |       289 
           |     72.66      27.34 |    100.00 
-----------+----------------------+----------
       yes |        14         40 |        54 
           |     25.93      74.07 |    100.00 
-----------+----------------------+----------
     Total |       224        119 |       343 
           |     65.31      34.69 |    100.00 

          Pearson chi2(1) =  43.8671   Pr = 0.000

. tab libmovement ethnicorder if startyear>1899, row chi

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |     Goal: ethnic
Leadership |    stratification
         : |       (reverse
  National |     ethnic/racial
liberation |      domination)
  movement |        no        yes |     Total
-----------+----------------------+----------
        no |       249         40 |       289 
           |     86.16      13.84 |    100.00 
-----------+----------------------+----------
       yes |        38         16 |        54 
           |     70.37      29.63 |    100.00 
-----------+----------------------+----------
     Total |       287         56 |       343 
           |     83.67      16.33 |    100.00 

          Pearson chi2(1) =   8.3026   Pr = 0.004

. 
. * =============================
. *  TABLE 4.2--regression table
. * =============================
. * create comparison sample for information criteria using model with smallest sample size 
. clear

. use revolutionaryepsmiopp.dta

. set seed 1234

. quietly: logit success lnparticnum i.urbandum##c.deathtile10 democrat antimonarch if startyear>1899, or nolog

. generate sample=e(sample)

. 
. * Model 1
. * Multiple imputation model
. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0171
                                                Largest FMI       =     0.0283
DF adjustment:   Large sample                   DF:     min       =  23,941.92
                                                        avg       =  24,136.32
                                                        max       =  24,330.73
Model F test:       Equal FMI                   F(   1,24330.7)   =      34.94
Within VCE type:          OIM                   Prob > F          =     0.0000

------------------------------------------------------------------------------
     success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 lnparticnum |   1.528654   .1097591     5.91   0.000     1.327972    1.759663
       _cons |   .0059561   .0046822    -6.52   0.000     .0012758    .0278063
------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb
(2 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(2 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           343     0.7202       0.0281        0.66509     0.77534

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. quietly:  logit success lnparticnum if startyear>1899 & sample==1, or nolog 

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        304 -199.5194  -183.6735       2     371.347    378.781
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        41            26  |         67
     -     |        70           167  |        237
-----------+--------------------------+-----------
   Total   |       111           193  |        304

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   36.94%
Specificity                     Pr( -|~D)   86.53%
Positive predictive value       Pr( D| +)   61.19%
Negative predictive value       Pr(~D| -)   70.46%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.47%
False - rate for true D         Pr( -| D)   63.06%
False + rate for classified +   Pr(~D| +)   38.81%
False - rate for classified -   Pr( D| -)   29.54%
--------------------------------------------------
Correctly classified                        68.42%
--------------------------------------------------

. 
. * Model 2
. mi estimate, post dots eform saving(miest, replace): logit success i.urbandum##c.deathtile10 if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0059
                                                Largest FMI       =     0.0086
DF adjustment:   Large sample                   DF:     min       = 258,392.89
                                                        avg       = 516,991.74
                                                        max       = 815,205.45
Model F test:       Equal FMI                   F(   3,992339.1)  =      13.08
Within VCE type:          OIM                   Prob > F          =     0.0000

----------------------------------------------------------------------------------------
               success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              urbandum |
                  yes  |   136.6561     130.19     5.16   0.000     21.12038    884.2124
           deathtile10 |   1.472991   .1636949     3.49   0.000     1.184691     1.83145
                       |
urbandum#c.deathtile10 |
                  yes  |   .5145065   .0676672    -5.05   0.000     .3975955    .6657945
                       |
                 _cons |   .0174434   .0157757    -4.48   0.000     .0029635    .1026721
----------------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb
(2 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(2 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           343     0.7277       0.0285        0.67192     0.78355

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. quietly: logit success i.urbandum##c.deathtile10 if startyear>1899 & sample==1, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        304 -199.5194  -182.6614       4    373.3227   388.1908
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        34            22  |         56
     -     |        77           171  |        248
-----------+--------------------------+-----------
   Total   |       111           193  |        304

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   30.63%
Specificity                     Pr( -|~D)   88.60%
Positive predictive value       Pr( D| +)   60.71%
Negative predictive value       Pr(~D| -)   68.95%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   11.40%
False - rate for true D         Pr( -| D)   69.37%
False + rate for classified +   Pr(~D| +)   39.29%
False - rate for classified -   Pr( D| -)   31.05%
--------------------------------------------------
Correctly classified                        67.43%
--------------------------------------------------

. 
. * Model 3
. logit success demonstrations strikes riots armednocivwar i.urbandum##i.civilwar if startyear>1899, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(7)        =      53.45
                                                Prob > chi2       =     0.0000
Log likelihood = -197.12141                     Pseudo R2         =     0.1194

-----------------------------------------------------------------------------------
          success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
   demonstrations |    6.25566   2.508115     4.57   0.000     2.850994    13.72619
          strikes |   1.913823   .6071236     2.05   0.041     1.027719     3.56393
            riots |   .7702947   .2431651    -0.83   0.408     .4149075    1.430087
    armednocivwar |    1.36104   .5040502     0.83   0.405     .6586207    2.812591
                  |
         urbandum |
             yes  |   3.550151   2.964397     1.52   0.129     .6910167    18.23917
                  |
         civilwar |
             yes  |   7.653286   6.930393     2.25   0.025     1.297319     45.1491
                  |
urbandum#civilwar |
         yes#yes  |   .1191556   .1142108    -2.22   0.026     .0182069    .7798158
                  |
            _cons |   .0423655   .0378794    -3.54   0.000     .0073442    .2443875
-----------------------------------------------------------------------------------

. lroc , nograph

Logistic model for success

number of observations =      343
area under ROC curve   =   0.7243

. quietly: logit success demonstrations strikes riots armednocivwar i.urbandum##i.civilwar if startyear>1899 & sam
> ple, or nolog

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        34            27  |         61
     -     |        77           166  |        243
-----------+--------------------------+-----------
   Total   |       111           193  |        304

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   30.63%
Specificity                     Pr( -|~D)   86.01%
Positive predictive value       Pr( D| +)   55.74%
Negative predictive value       Pr(~D| -)   68.31%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.99%
False - rate for true D         Pr( -| D)   69.37%
False + rate for classified +   Pr(~D| +)   44.26%
False - rate for classified -   Pr( D| -)   31.69%
--------------------------------------------------
Correctly classified                        65.79%
--------------------------------------------------

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        304 -199.5194  -183.6438       8    383.2876   413.0238
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. 
. * Model 4
. logit success democrat leftist independ antimonarch ethnicorder islamist if startyear>1899, or nolog 

Logistic regression                             Number of obs     =        343
                                                LR chi2(6)        =      47.78
                                                Prob > chi2       =     0.0000
Log likelihood =  -199.9555                     Pseudo R2         =     0.1067

------------------------------------------------------------------------------
     success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    democrat |   3.599876   1.157272     3.98   0.000      1.91711    6.759711
     leftist |   .8109078   .2583955    -0.66   0.511     .4342465    1.514281
    independ |   .6440757   .2036165    -1.39   0.164     .3466093    1.196833
 antimonarch |   2.879657   1.240479     2.46   0.014     1.237844    6.699087
 ethnicorder |   1.670605   .5693819     1.51   0.132     .8565724    3.258243
    islamist |     .47678   .2527444    -1.40   0.162     .1686903    1.347553
       _cons |   .4113077   .1130102    -3.23   0.001     .2400448    .7047603
------------------------------------------------------------------------------

. lroc , nograph

Logistic model for success

number of observations =      343
area under ROC curve   =   0.7126

. quietly: logit success democrat leftist independ antimonarch ethnicorder islamist if startyear>1899 & sample, or
>  nolog

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        53            35  |         88
     -     |        58           158  |        216
-----------+--------------------------+-----------
   Total   |       111           193  |        304

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   47.75%
Specificity                     Pr( -|~D)   81.87%
Positive predictive value       Pr( D| +)   60.23%
Negative predictive value       Pr(~D| -)   73.15%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   18.13%
False - rate for true D         Pr( -| D)   52.25%
False + rate for classified +   Pr(~D| +)   39.77%
False - rate for classified -   Pr( D| -)   26.85%
--------------------------------------------------
Correctly classified                        69.41%
--------------------------------------------------

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        304 -199.5194  -181.7464       7    377.4927   403.5119
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. 
. * Model 5
. logit success coalitionleadership vanguard othpolpartylead traditionalleadership paramilorg underground libmovem
> ent if startyear>1899, or nolog

Logistic regression                             Number of obs     =        343
                                                LR chi2(7)        =      34.55
                                                Prob > chi2       =     0.0000
Log likelihood = -206.56998                     Pseudo R2         =     0.0772

---------------------------------------------------------------------------------------
              success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
  coalitionleadership |   3.000136   1.047301     3.15   0.002      1.51356    5.946786
             vanguard |   .5647716   .2471703    -1.31   0.192     .2395232    1.331675
      othpolpartylead |   .7824141   .3473973    -0.55   0.581     .3277158    1.867996
traditionalleadership |   .3330452   .1564008    -2.34   0.019     .1326697    .8360545
           paramilorg |   .7341257   .2688071    -0.84   0.399     .3581747    1.504686
          underground |   .5658809   .2577612    -1.25   0.211      .231739    1.381819
          libmovement |    1.10405   .4030194     0.27   0.786     .5398441    2.257922
                _cons |   .5828763   .1525212    -2.06   0.039     .3490138    .9734422
---------------------------------------------------------------------------------------

. lroc , nograph

Logistic model for success

number of observations =      343
area under ROC curve   =   0.6764

. quietly:  logit success coalitionleadership vanguard othpolpartylead traditionalleadership paramilorg undergroun
> d libmovement if startyear>1899 & sample, or nolog

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        36            24  |         60
     -     |        75           169  |        244
-----------+--------------------------+-----------
   Total   |       111           193  |        304

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   32.43%
Specificity                     Pr( -|~D)   87.56%
Positive predictive value       Pr( D| +)   60.00%
Negative predictive value       Pr(~D| -)   69.26%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   12.44%
False - rate for true D         Pr( -| D)   67.57%
False + rate for classified +   Pr(~D| +)   40.00%
False - rate for classified -   Pr( D| -)   30.74%
--------------------------------------------------
Correctly classified                        67.43%
--------------------------------------------------

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        304 -199.5194  -188.1802       8    392.3604   422.0967
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. 
. * Model 6
. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum i.urbandum##c.deathtile10 demonst
> rations strikes civilwar democrat antimonarch coalitionleadership traditionalleadership if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0059
                                                Largest FMI       =     0.0305
DF adjustment:   Large sample                   DF:     min       =  20,568.19
                                                        avg       = 5874686.56
                                                        max       =   5.90e+07
Model F test:       Equal FMI                   F(  11, 5.3e+06)  =       5.91
Within VCE type:          OIM                   Prob > F          =     0.0000

----------------------------------------------------------------------------------------
               success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
           lnparticnum |   1.315324   .1296292     2.78   0.005     1.084275    1.595608
                       |
              urbandum |
                  yes  |   16.31347    18.5343     2.46   0.014     1.759805    151.2266
           deathtile10 |    1.28691   .1589807     2.04   0.041     1.010165    1.639473
                       |
urbandum#c.deathtile10 |
                  yes  |   .5743116   .0828973    -3.84   0.000     .4327959    .7621002
                       |
        demonstrations |   3.555482   1.609356     2.80   0.005     1.464232    8.633506
               strikes |   2.016088   .7104095     1.99   0.047     1.010581    4.022052
              civilwar |   4.013419   2.297707     2.43   0.015     1.306754    12.32637
              democrat |   2.433301   .9115246     2.37   0.018     1.167702    5.070604
           antimonarch |   2.769086   1.391762     2.03   0.043     1.033986    7.415801
   coalitionleadership |   1.118954    .416435     0.30   0.763     .5395409    2.320598
 traditionalleadership |   .5066721   .2399984    -1.44   0.151     .2002319    1.282096
                 _cons |    .000831   .0010619    -5.55   0.000     .0000679    .0101693
----------------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb
(2 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(2 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           343     0.8048       0.0241        0.75760     0.85209

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. quietly: logit success lnparticnum i.urbandum##c.deathtile10 demonstrations strikes civilwar democrat antimonarc
> h coalitionleadership traditionalleadership if startyear>1899, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        304 -199.5194  -164.6394      12    353.2788   397.8831
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        59            31  |         90
     -     |        52           162  |        214
-----------+--------------------------+-----------
   Total   |       111           193  |        304

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   53.15%
Specificity                     Pr( -|~D)   83.94%
Positive predictive value       Pr( D| +)   65.56%
Negative predictive value       Pr(~D| -)   75.70%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   16.06%
False - rate for true D         Pr( -| D)   46.85%
False + rate for classified +   Pr(~D| +)   34.44%
False - rate for classified -   Pr( D| -)   24.30%
--------------------------------------------------
Correctly classified                        72.70%
--------------------------------------------------

. 
. * Model 7
. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum i.urbandum##c.deathtile10 demonst
> rations strikes civilwar democrat antimonarch if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0068
                                                Largest FMI       =     0.0306
DF adjustment:   Large sample                   DF:     min       =  20,377.43
                                                        avg       = 852,731.34
                                                        max       = 4459723.75
Model F test:       Equal FMI                   F(   9, 3.1e+06)  =       7.12
Within VCE type:          OIM                   Prob > F          =     0.0000

----------------------------------------------------------------------------------------
               success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
           lnparticnum |    1.32269   .1285724     2.88   0.004     1.093231     1.60031
                       |
              urbandum |
                  yes  |   18.53091   21.03551     2.57   0.010     2.002838     171.454
           deathtile10 |   1.287144   .1595506     2.04   0.042     1.009516    1.641123
                       |
urbandum#c.deathtile10 |
                  yes  |   .5638201   .0807644    -4.00   0.000     .4258041    .7465712
                       |
        demonstrations |   3.654176   1.628343     2.91   0.004     1.525752    8.751753
               strikes |   2.010159   .7023156     2.00   0.046     1.013526    3.986814
              civilwar |   4.087759   2.313473     2.49   0.013     1.348169    12.39442
              democrat |   2.538741   .9280224     2.55   0.011     1.240127    5.197217
           antimonarch |   2.746393   1.365274     2.03   0.042      1.03661     7.27629
                 _cons |   .0007008   .0008872    -5.74   0.000     .0000586    .0083781
----------------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb
(2 missing values generated)

. generate prsuccmi = invlogit(xbsuccmi)
(2 missing values generated)

. * Obtain accuracy of prediction
. * Area under the ROC curve
. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           343     0.8005       0.0243        0.75294     0.84806

. drop xbsuccmi

. drop prsuccmi

. * AIC and BIC on common complete-case model
. quietly: logit success lnparticnum i.urbandum##c.deathtile10 demonstrations strikes civilwar democrat antimonarc
> h if startyear>1899, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        304 -199.5194  -165.4411      10    350.8821   388.0524
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * True positive and false positive rates on complete-case sample
. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        55            32  |         87
     -     |        56           161  |        217
-----------+--------------------------+-----------
   Total   |       111           193  |        304

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   49.55%
Specificity                     Pr( -|~D)   83.42%
Positive predictive value       Pr( D| +)   63.22%
Negative predictive value       Pr(~D| -)   74.19%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   16.58%
False - rate for true D         Pr( -| D)   50.45%
False + rate for classified +   Pr(~D| +)   36.78%
False - rate for classified -   Pr( D| -)   25.81%
--------------------------------------------------
Correctly classified                        71.05%
--------------------------------------------------

. 
. * Likelihood ratio test between Model 7 and Model 1 (Model 1 has lower BIC; Model 7 has lower AIC)
. * Model 1
. quietly: logit success lnparticnum if startyear>1899 & sample, nolog or

. estimates store mod1

. * Model 7
. quietly: logit success lnparticnum i.urbandum##c.deathtile10 demonstrations strikes civilwar democrat antimonarc
> h if startyear>1899 & sample, or nolog

. estimates store mod7

. lrtest mod1 mod7

Likelihood-ratio test                                 LR chi2(8)  =     36.46
(Assumption: mod1 nested in mod7)                     Prob > chi2 =    0.0000

. *  RESULT:  Superiority of Model 7 over nested Model 1 shown
. drop _est_mod1 _est_mod7

. 
. * Likelihood ratio test between Model 7 and Model 2 (Model 2 has lower BIC; Model 7 has lower AIC)
. * Model 2
. quietly: logit success i.urbandum##c.deathtile10 if startyear>1899 & sample, nolog or

. estimates store mod2

. * Model 7
. quietly: logit success lnparticnum i.urbandum##c.deathtile10 demonstrations strikes civilwar democrat antimonarc
> h if startyear>1899 & sample, or nolog

. estimates store mod7

. lrtest mod2 mod7

Likelihood-ratio test                                 LR chi2(6)  =     34.44
(Assumption: mod2 nested in mod7)                     Prob > chi2 =    0.0000

. *  RESULT:  Superiority of Model 7 over nested Model 2 shown
. drop _est_mod2 _est_mod7

. * clear the sample selection variable for comparing information criteria
. drop sample

. 
. * ========================================================================
. * FIGURE 4.10--ROC curves for opposition tactics, by urban/rural episodes
. * ========================================================================
. * Complete-case sample
. * Figure 4.10a--opposition factors, by urban/rural
. clear

. use revolutionaryeps.dta

. quietly: logit success lnparticnum i.urbandum##c.deathtile10 demonstrations strikes civilwar democrat antimonarc
> h if startyear>1899, or nolog

. generate sample=e(sample)

. * Accuracy of model
. predict xbsucc if sample==1, xb
(41 missing values generated)

. roccomp success xbsucc if sample==1, by(urbandum) graph summary legend(position(6) order (1 2) cols(2)) ysize(6)
>  xsize(6) plotregion(lcolor(black)) ylabel(, nogrid labsize(small)) xlabel(, nogrid labsize(small)) ytick(0(.1)1
> ) xtick(0(.1)1) ylabel(0(.1)1) xlabel(0(.1)1) title({bf: 4.10a: Complete case sample} , size(medlarge)) ytitle({
> bf: True positive rate} , size(medsmall)) xtitle({bf: False positive rate} , size(medsmall)) rlopts(lpattern(das
> h)) plot1opts(msize(medlarge)) plot2opts(msize(medlarge))

                              ROC                    -Asymptotic Normal--
urbandum           Obs       Area     Std. Err.      [95% Conf. Interval]
-------------------------------------------------------------------------
0                  148     0.7332       0.0467        0.64171     0.82467
1                  156     0.7525       0.0383        0.67752     0.82747
-------------------------------------------------------------------------
Ho: area(0) = area(1)
    chi2(1) =     0.10       Prob>chi2 =   0.7491

. graph export Logfiles\figure4_10a.pdf, replace
(file Logfiles\figure4_10a.pdf written in PDF format)

. * Figure manipulated in Stata graph editor
. 
. * Imputed sample
. * Figure 4.10b--regime factors, by urban/rural
. clear

. use revolutionaryepsmiopp.dta

. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum urbandum deathtile10 urbxdeathtil
> e10 demonstrations strikes civilwar democrat antimonarch if startyear>1899

Imputations (20):
  .........10.........20 done

Multiple-imputation estimates                   Imputations       =         20
Logistic regression                             Number of obs     =        343
                                                Average RVI       =     0.0066
                                                Largest FMI       =     0.0297
DF adjustment:   Large sample                   DF:     min       =  21,620.97
                                                        avg       = 928,988.54
                                                        max       = 4050922.39
Model F test:       Equal FMI                   F(   9, 3.3e+06)  =       7.11
Within VCE type:          OIM                   Prob > F          =     0.0000

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    lnparticnum |   1.321696   .1285057     2.87   0.004     1.092362    1.599178
       urbandum |   18.71207   21.23897     2.58   0.010     2.022881    173.0905
    deathtile10 |   1.287879   .1595311     2.04   0.041     1.010263    1.641783
urbxdeathtile10 |   .5632313    .080623    -4.01   0.000      .425444    .7456432
 demonstrations |   3.643785    1.62417     2.90   0.004      1.52104    8.729009
        strikes |     2.0082   .7017091     2.00   0.046     1.012461    3.983231
       civilwar |   4.083492   2.312382     2.48   0.013     1.345906    12.38935
       democrat |   2.536406   .9275022     2.55   0.011     1.238667    5.193776
    antimonarch |   2.746739   1.365315     2.03   0.042     1.036838    7.276523
          _cons |   .0007041   .0008909    -5.74   0.000      .000059    .0084059
---------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb
(2 missing values generated)

. roccomp success xbsuccmi, by(urbandum) graph summary legend(position(6) order (1 2) cols(2)) ysize(6) xsize(6) p
> lotregion(lcolor(black)) ylabel(, nogrid labsize(small)) xlabel(, nogrid labsize(small)) ytick(0(.1)1) xtick(0(.
> 1)1) ylabel(0(.1)1) xlabel(0(.1)1) title({bf: 4.10b: Multiple imputation (20 samples)} , size(medlarge)) ytitle(
> {it: True positive rate} , size(medsmall)) xtitle({it: False positive rate} , size(medsmall)) rlopts(lpattern(da
> sh)) plot1opts(msize(medlarge)) plot2opts(msize(medlarge))

                              ROC                    -Asymptotic Normal--
urbandum           Obs       Area     Std. Err.      [95% Conf. Interval]
-------------------------------------------------------------------------
0                  163     0.7460       0.0456        0.65655     0.83545
1                  180     0.7928       0.0330        0.72820     0.85742
-------------------------------------------------------------------------
Ho: area(0) = area(1)
    chi2(1) =     0.69       Prob>chi2 =   0.4058

. graph export Logfiles\figure4_10b.pdf, replace
(file Logfiles\figure4_10b.pdf written in PDF format)

. * Figures manipulated in Stata graph editor
. * Figures combined in Stata graph editor and edited
. 
. 
. * =============================================
. * TABLE 4.3--combined models; regression table
. * =============================================
. * create comparison sample for information criteria using complete case model
. clear

. use revolutionaryepsmicomb.dta

. set seed 1234

. quietly: logit success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations strikes democrat antimona
> rch newpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmi
> lexp if startyear>1899, or nolog

. generate sample=e(sample)

. 
. * Opposition model
. * Complete case sample
. logit success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations strikes civilwar democrat antimona
> rch if startyear>1899, or nolog

Logistic regression                             Number of obs     =        255
                                                LR chi2(9)        =      64.70
                                                Prob > chi2       =     0.0000
Log likelihood = -137.03282                     Pseudo R2         =     0.1910

---------------------------------------------------------------------------------
        success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    lnparticnum |   1.291565   .1411906     2.34   0.019     1.042473    1.600175
       urbandum |   15.40874   20.32619     2.07   0.038     1.161231    204.4635
    deathtile10 |   1.341449   .1921708     2.05   0.040     1.013058     1.77629
urbxdeathtile10 |   .5758363   .0945276    -3.36   0.001     .4174153    .7943827
 demonstrations |   4.133264   2.057656     2.85   0.004     1.557905    10.96593
        strikes |   1.716285   .6668366     1.39   0.164     .8014345    3.675453
       civilwar |   2.526567   1.588155     1.47   0.140     .7370228    8.661255
       democrat |   2.038674   .8097245     1.79   0.073     .9359789    4.440478
    antimonarch |   2.555116   1.475921     1.62   0.104     .8236219    7.926716
          _cons |   .0010093   .0014671    -4.75   0.000     .0000584    .0174324
---------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        53            29  |         82
     -     |        44           129  |        173
-----------+--------------------------+-----------
   Total   |        97           158  |        255

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   54.64%
Specificity                     Pr( -|~D)   81.65%
Positive predictive value       Pr( D| +)   64.63%
Negative predictive value       Pr(~D| -)   74.57%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   18.35%
False - rate for true D         Pr( -| D)   45.36%
False + rate for classified +   Pr(~D| +)   35.37%
False - rate for classified -   Pr( D| -)   25.43%
--------------------------------------------------
Correctly classified                        71.37%
--------------------------------------------------

. lroc , nograph

Logistic model for success

number of observations =      255
area under ROC curve   =   0.7871

. * Leave-one-out cross-validated AUC
. looclass success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations strikes civilwar democrat antim
> onarch if startyear>1899, model (logit)


Iterating across (255) observations
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
.....


Classification Table for Full Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        53            29  |         82
     -     |        44           129  |        173
-----------+--------------------------+-----------
   Total   |        97           158  |        255



Classification Table for Test Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        53            32  |         85
     -     |        44           126  |        170
-----------+--------------------------+-----------
   Total   |        97           158  |        255



Classified + if predicted Pr(D) >= .5
True D defined as  != 0
                                            Full         Test
----------------------------------------------------------------
Sensitivity                     Pr( +| D)   54.64%       54.64%
Specificity                     Pr( -|~D)   81.65%       79.75%
Positive predictive value       Pr( D| +)   64.63%       62.35%
Negative predictive value       Pr(~D| -)   74.57%       74.12%
----------------------------------------------------------------
False + rate for true ~D        Pr( +|~D)   18.35%       20.25%
False - rate for true D         Pr( -| D)   45.36%       45.36%
False + rate for classified +   Pr(~D| +)   35.37%       37.65%
False - rate for classified -   Pr( D| -)   25.43%       25.88%
----------------------------------------------------------------
Correctly classified                        71.37%       70.20%
----------------------------------------------------------------
ROC area                                    0.7871       0.7438
----------------------------------------------------------------
p-value for Full vs Test ROC areas                       0.0000
----------------------------------------------------------------

. * AIC and BIC on common sample
. quietly: logit success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations strikes civilwar democrat
>  antimonarch if startyear>1899 & sample==1, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        212 -141.9167  -110.0443      10    240.0886   273.6545
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Multiple imputation
. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum urbandum deathtile10 urbxdeathtil
> e10 demonstrations strikes civilwar democrat antimonarch if startyear>1899

Imputations (30):
  .........10.........20.........30 done

Multiple-imputation estimates                   Imputations       =         30
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0064
                                                Largest FMI       =     0.0377
DF adjustment:   Large sample                   DF:     min       =  20,541.74
                                                        avg       = 1308987.30
                                                        max       = 2321197.11
Model F test:       Equal FMI                   F(   9, 5.6e+06)  =       6.61
Within VCE type:          OIM                   Prob > F          =     0.0000

---------------------------------------------------------------------------------
        success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
    lnparticnum |   1.299088    .140254     2.42   0.015     1.051319     1.60525
       urbandum |   20.26103   26.75569     2.28   0.023     1.522675    269.5976
    deathtile10 |    1.40259   .2057777     2.31   0.021     1.052079    1.869877
urbxdeathtile10 |   .5431514   .0892318    -3.72   0.000     .3936234    .7494814
 demonstrations |   5.139998   2.481967     3.39   0.001     1.994988    13.24298
        strikes |   1.973251   .7338373     1.83   0.068     .9519772    4.090138
       civilwar |    2.43905   1.472687     1.48   0.140     .7469113    7.964757
       democrat |    2.21631   .8455729     2.09   0.037     1.049246    4.681484
    antimonarch |   2.708074    1.46155     1.85   0.065     .9402987    7.799293
          _cons |   .0006082   .0008819    -5.11   0.000     .0000355    .0104304
---------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb

. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.8168       0.0252        0.76739     0.86620

. drop xbsuccmi

. 
. * Regime model
. * Complete-case sample
. logit success newpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile civilwar
>  newcivxmilexp if startyear>1899, or nolog

Logistic regression                             Number of obs     =        234
                                                LR chi2(8)        =      73.79
                                                Prob > chi2       =     0.0000
Log likelihood =  -118.0399                     Pseudo R2         =     0.2381

-------------------------------------------------------------------------------------
            success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .9043827   .0312222    -2.91   0.004     .8452127     .967695
    newpolitymin1sq |    .973352   .0069052    -3.81   0.000     .9599117    .9869805
  newincumbpowerdur |   1.063515   .0212121     3.09   0.002     1.022743    1.105914
        newgdppcthl |   .8621668   .0705696    -1.81   0.070     .7343772    1.012193
          newlnoill |   .8692383    .034219    -3.56   0.000     .8046924    .9389615
newmilexpsold10tile |    1.49645   .1405138     4.29   0.000     1.244905    1.798823
           civilwar |   1.028174   .7779181     0.04   0.971     .2333705    4.529884
      newcivxmilexp |   .8080672   .1021405    -1.69   0.092     .6307463    1.035238
              _cons |   .3343246   .1664207    -2.20   0.028     .1260248    .8869127
-------------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        57            20  |         77
     -     |        31           126  |        157
-----------+--------------------------+-----------
   Total   |        88           146  |        234

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   64.77%
Specificity                     Pr( -|~D)   86.30%
Positive predictive value       Pr( D| +)   74.03%
Negative predictive value       Pr(~D| -)   80.25%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.70%
False - rate for true D         Pr( -| D)   35.23%
False + rate for classified +   Pr(~D| +)   25.97%
False - rate for classified -   Pr( D| -)   19.75%
--------------------------------------------------
Correctly classified                        78.21%
--------------------------------------------------

. lroc , nograph

Logistic model for success

number of observations =      234
area under ROC curve   =   0.8150

. * Leave-one-out cross-validated AUC
. looclass success newpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile civil
> war newcivxmilexp if startyear>1899, model (logit)


Iterating across (234) observations
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................


Classification Table for Full Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        57            20  |         77
     -     |        31           126  |        157
-----------+--------------------------+-----------
   Total   |        88           146  |        234



Classification Table for Test Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        55            27  |         82
     -     |        33           119  |        152
-----------+--------------------------+-----------
   Total   |        88           146  |        234



Classified + if predicted Pr(D) >= .5
True D defined as  != 0
                                            Full         Test
----------------------------------------------------------------
Sensitivity                     Pr( +| D)   64.77%       62.50%
Specificity                     Pr( -|~D)   86.30%       81.51%
Positive predictive value       Pr( D| +)   74.03%       67.07%
Negative predictive value       Pr(~D| -)   80.25%       78.29%
----------------------------------------------------------------
False + rate for true ~D        Pr( +|~D)   13.70%       18.49%
False - rate for true D         Pr( -| D)   35.23%       37.50%
False + rate for classified +   Pr(~D| +)   25.97%       32.93%
False - rate for classified -   Pr( D| -)   19.75%       21.71%
----------------------------------------------------------------
Correctly classified                        78.21%       74.36%
----------------------------------------------------------------
ROC area                                    0.8150       0.7799
----------------------------------------------------------------
p-value for Full vs Test ROC areas                       0.0000
----------------------------------------------------------------

. * AIC and BIC on common sample
. quietly: logit success newpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile
>  civilwar newcivxmilexp if startyear>1899 & sample, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        212 -141.9167  -110.0145       9     238.029   268.2382
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Multiple imputation
. mi estimate, post dots eform saving(miest, replace): logit success newpolitymin1 newpolitymin1sq newincumbpowerd
> ur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp if startyear>1899

Imputations (30):
  .........10.........20.........30 done

Multiple-imputation estimates                   Imputations       =         30
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0751
                                                Largest FMI       =     0.1406
DF adjustment:   Large sample                   DF:     min       =   1,491.11
                                                        avg       =  14,124.69
                                                        max       =  65,086.52
Model F test:       Equal FMI                   F(   8,38459.5)   =       6.05
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
      newpolitymin1 |   .8967495   .0284745    -3.43   0.001     .8426236    .9543522
    newpolitymin1sq |    .979785   .0062023    -3.23   0.001      .967694     .992027
  newincumbpowerdur |   1.043162   .0179879     2.45   0.014     1.008495    1.079021
        newgdppcthl |   .8565314    .065796    -2.02   0.044        .7368    .9957194
          newlnoill |     .89191   .0315894    -3.23   0.001     .8320937    .9560263
newmilexpsold10tile |   1.414153   .1229452     3.99   0.000     1.192491    1.677016
           civilwar |   .6287469   .4283794    -0.68   0.496     .1652976    2.391582
      newcivxmilexp |   .8732891    .100892    -1.17   0.241     .6962264    1.095382
              _cons |   .4007653   .1811156    -2.02   0.043     .1652556    .9719054
-------------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb

. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.8029       0.0267        0.75055     0.85532

. drop xbsuccmi

. 
. * Combined model 1
. * Complete-case sample
. logit success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations strikes democrat antimonarch newpo
> litymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp if s
> tartyear>1899, or nolog

Logistic regression                             Number of obs     =        212
                                                LR chi2(16)       =     109.94
                                                Prob > chi2       =     0.0000
Log likelihood = -86.948341                     Pseudo R2         =     0.3873

-------------------------------------------------------------------------------------
            success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
        lnparticnum |    1.64979   .2673858     3.09   0.002     1.200804    2.266653
           urbandum |   77.06884   138.3682     2.42   0.016     2.283732    2600.834
        deathtile10 |   1.333583   .2487816     1.54   0.123     .9251824    1.922263
    urbxdeathtile10 |   .4747546   .1087124    -3.25   0.001     .3030788    .7436743
     demonstrations |   10.37929   7.392216     3.29   0.001     2.570013    41.91798
            strikes |   .7501341   .4026525    -0.54   0.592     .2619607    2.148037
           democrat |   .5853858   .3276911    -0.96   0.339     .1954119    1.753612
        antimonarch |   .7228298   .5841976    -0.40   0.688     .1482816    3.523586
      newpolitymin1 |   .9086163   .0372741    -2.34   0.019     .8384202    .9846895
    newpolitymin1sq |   .9738398   .0083576    -3.09   0.002     .9575962     .990359
  newincumbpowerdur |   1.069717   .0299261     2.41   0.016     1.012642    1.130009
        newgdppcthl |   .7307502    .076511    -3.00   0.003     .5951774    .8972045
          newlnoill |   .8541233   .0442561    -3.04   0.002      .771642    .9454211
newmilexpsold10tile |   1.148007   .1435045     1.10   0.270     .8985488    1.466721
           civilwar |   1.109363   1.299528     0.09   0.929      .111676    11.02014
      newcivxmilexp |   1.186407   .1972009     1.03   0.304     .8565437    1.643303
              _cons |   .0001359   .0002704    -4.48   0.000     2.75e-06     .006709
-------------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        64            15  |         79
     -     |        19           114  |        133
-----------+--------------------------+-----------
   Total   |        83           129  |        212

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   77.11%
Specificity                     Pr( -|~D)   88.37%
Positive predictive value       Pr( D| +)   81.01%
Negative predictive value       Pr(~D| -)   85.71%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   11.63%
False - rate for true D         Pr( -| D)   22.89%
False + rate for classified +   Pr(~D| +)   18.99%
False - rate for classified -   Pr( D| -)   14.29%
--------------------------------------------------
Correctly classified                        83.96%
--------------------------------------------------

. lroc , nograph

Logistic model for success

number of observations =      212
area under ROC curve   =   0.8906

. * Leave-one-out cross-validated AUC--overfitting evident
. looclass success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations strikes democrat antimonarch ne
> wpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmilexp i
> f startyear>1899, model (logit)


Iterating across (212) observations
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
............


Classification Table for Full Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        64            15  |         79
     -     |        19           114  |        133
-----------+--------------------------+-----------
   Total   |        83           129  |        212



Classification Table for Test Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        55            19  |         74
     -     |        28           110  |        138
-----------+--------------------------+-----------
   Total   |        83           129  |        212



Classified + if predicted Pr(D) >= .5
True D defined as  != 0
                                            Full         Test
----------------------------------------------------------------
Sensitivity                     Pr( +| D)   77.11%       66.27%
Specificity                     Pr( -|~D)   88.37%       85.27%
Positive predictive value       Pr( D| +)   81.01%       74.32%
Negative predictive value       Pr(~D| -)   85.71%       79.71%
----------------------------------------------------------------
False + rate for true ~D        Pr( +|~D)   11.63%       14.73%
False - rate for true D         Pr( -| D)   22.89%       33.73%
False + rate for classified +   Pr(~D| +)   18.99%       25.68%
False - rate for classified -   Pr( D| -)   14.29%       20.29%
----------------------------------------------------------------
Correctly classified                        83.96%       77.83%
----------------------------------------------------------------
ROC area                                    0.8906       0.8352
----------------------------------------------------------------
p-value for Full vs Test ROC areas                       0.0000
----------------------------------------------------------------

. * AIC and BIC on common sample
. quietly: logit success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations strikes democrat antimona
> rch newpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile civilwar newcivxmi
> lexp if startyear>1899 & sample, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        212 -141.9167  -86.94834      17    207.8967   264.9586
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Multiple imputation
. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum urbandum deathtile10 urbxdeathtil
> e10 demonstrations strikes democrat antimonarch newpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newl
> noill newmilexpsold10tile civilwar newcivxmilexp if startyear>1899

Imputations (30):
  .........10.........20.........30 done

Multiple-imputation estimates                   Imputations       =         30
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0645
                                                Largest FMI       =     0.1598
DF adjustment:   Large sample                   DF:     min       =   1,156.04
                                                        avg       =  19,491.05
                                                        max       =  99,809.79
Model F test:       Equal FMI                   F(  16,118195.3)  =       4.13
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
        lnparticnum |   1.651743   .2345072     3.53   0.000     1.250425    2.181862
           urbandum |   24.58328   37.29409     2.11   0.035     1.256648    480.9123
        deathtile10 |   1.326261    .220387     1.70   0.089      .957569     1.83691
    urbxdeathtile10 |    .529353   .1019618    -3.30   0.001     .3628901    .7721749
     demonstrations |   7.784168   4.532957     3.52   0.000     2.486113    24.37269
            strikes |    1.30953   .5833312     0.61   0.545     .5469355    3.135411
           democrat |   .7897611   .3794189    -0.49   0.623     .3079704    2.025268
        antimonarch |   1.752913   1.188873     0.83   0.408     .4639223    6.623312
      newpolitymin1 |   .8923718   .0334595    -3.04   0.002     .8291253    .9604429
    newpolitymin1sq |    .978567   .0071915    -2.95   0.003     .9645641    .9927731
  newincumbpowerdur |   1.030479   .0227696     1.36   0.174      .986802    1.076088
        newgdppcthl |   .7703404    .070035    -2.87   0.004     .6446027    .9206049
          newlnoill |   .8721528   .0406125    -2.94   0.003     .7960525    .9555282
newmilexpsold10tile |   1.210308   .1333261     1.73   0.083     .9752099    1.502083
           civilwar |   .9185577   .9023964    -0.09   0.931     .1338031    6.305894
      newcivxmilexp |   1.152809   .1718847     0.95   0.340     .8604184    1.544562
              _cons |   .0001262   .0002215    -5.11   0.000     4.04e-06    .0039435
-------------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb

. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.8903       0.0189        0.85332     0.92731

. drop xbsuccmi

. 
. * Combined model 2 (reduced)
. * Complete-case sample
. logit success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations newpolitymin1 newpolitymin1sq newi
> ncumbpowerdur newgdppcthl newlnoill newmilexpsold10tile if startyear>1899, or nolog

Logistic regression                             Number of obs     =        212
                                                LR chi2(11)       =     107.05
                                                Prob > chi2       =     0.0000
Log likelihood = -88.389837                     Pseudo R2         =     0.3772

-------------------------------------------------------------------------------------
            success | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
        lnparticnum |   1.494632   .2182067     2.75   0.006     1.122702    1.989776
           urbandum |   23.99171     37.495     2.03   0.042     1.121441    513.2699
        deathtile10 |   1.386254   .2406274     1.88   0.060     .9864845    1.948028
    urbxdeathtile10 |   .5291342   .1105543    -3.05   0.002     .3513344    .7969134
     demonstrations |   7.200039   4.665434     3.05   0.002     2.021989    25.63839
      newpolitymin1 |   .9223947    .036431    -2.05   0.041     .8536849    .9966346
    newpolitymin1sq |   .9746817   .0080553    -3.10   0.002     .9590207    .9905985
  newincumbpowerdur |    1.05959   .0255015     2.40   0.016     1.010768    1.110769
        newgdppcthl |    .735349    .073386    -3.08   0.002      .604708    .8942136
          newlnoill |   .8570414   .0411406    -3.21   0.001     .7800842    .9415905
newmilexpsold10tile |   1.278636   .1084534     2.90   0.004       1.0828    1.509891
              _cons |   .0004135   .0007174    -4.49   0.000     .0000138    .0123908
-------------------------------------------------------------------------------------

. estat classification

Logistic model for success

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        60            14  |         74
     -     |        23           115  |        138
-----------+--------------------------+-----------
   Total   |        83           129  |        212

Classified + if predicted Pr(D) >= .5
True D defined as success != 0
--------------------------------------------------
Sensitivity                     Pr( +| D)   72.29%
Specificity                     Pr( -|~D)   89.15%
Positive predictive value       Pr( D| +)   81.08%
Negative predictive value       Pr(~D| -)   83.33%
--------------------------------------------------
False + rate for true ~D        Pr( +|~D)   10.85%
False - rate for true D         Pr( -| D)   27.71%
False + rate for classified +   Pr(~D| +)   18.92%
False - rate for classified -   Pr( D| -)   16.67%
--------------------------------------------------
Correctly classified                        82.55%
--------------------------------------------------

. lroc , nograph

Logistic model for success

number of observations =      212
area under ROC curve   =   0.8852

. * Leave-one-out cross-validated AUC
. looclass success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations  newpolitymin1 newpolitymin1sq 
> newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile if startyear>1899, model (logit)


Iterating across (212) observations
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
............


Classification Table for Full Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        60            14  |         74
     -     |        23           115  |        138
-----------+--------------------------+-----------
   Total   |        83           129  |        212



Classification Table for Test Data:

              -------- True --------
Classified |         D            ~D  |      Total
-----------+--------------------------+-----------
     +     |        57            17  |         74
     -     |        26           112  |        138
-----------+--------------------------+-----------
   Total   |        83           129  |        212



Classified + if predicted Pr(D) >= .5
True D defined as  != 0
                                            Full         Test
----------------------------------------------------------------
Sensitivity                     Pr( +| D)   72.29%       68.67%
Specificity                     Pr( -|~D)   89.15%       86.82%
Positive predictive value       Pr( D| +)   81.08%       77.03%
Negative predictive value       Pr(~D| -)   83.33%       81.16%
----------------------------------------------------------------
False + rate for true ~D        Pr( +|~D)   10.85%       13.18%
False - rate for true D         Pr( -| D)   27.71%       31.33%
False + rate for classified +   Pr(~D| +)   18.92%       22.97%
False - rate for classified -   Pr( D| -)   16.67%       18.84%
----------------------------------------------------------------
Correctly classified                        82.55%       79.72%
----------------------------------------------------------------
ROC area                                    0.8852       0.8456
----------------------------------------------------------------
p-value for Full vs Test ROC areas                       0.0000
----------------------------------------------------------------

. * AIC and BIC on common sample
. quietly: logit success lnparticnum urbandum deathtile10 urbxdeathtile10 demonstrations  newpolitymin1 newpolitym
> in1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile if startyear>1899 & sample, or nolog

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |        Obs  ll(null)  ll(model)      df         AIC        BIC
-------------+---------------------------------------------------------------
           . |        212 -141.9167  -88.38984      12    200.7797   241.0587
-----------------------------------------------------------------------------
               Note: N=Obs used in calculating BIC; see [R] BIC note.

. * Multiple imputation
. mi estimate, post dots eform saving(miest, replace): logit success lnparticnum urbandum deathtile10 urbxdeathtil
> e10 demonstrations newpolitymin1 newpolitymin1sq newincumbpowerdur newgdppcthl newlnoill newmilexpsold10tile if 
> startyear>1899

Imputations (30):
  .........10.........20.........30 done

Multiple-imputation estimates                   Imputations       =         30
Logistic regression                             Number of obs     =        288
                                                Average RVI       =     0.0621
                                                Largest FMI       =     0.2000
DF adjustment:   Large sample                   DF:     min       =     740.78
                                                        avg       =  23,570.12
                                                        max       = 120,162.55
Model F test:       Equal FMI                   F(  11,83124.7)   =       6.07
Within VCE type:          OIM                   Prob > F          =     0.0000

-------------------------------------------------------------------------------------
            success |     exp(b)   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
        lnparticnum |   1.581065   .2037005     3.56   0.000     1.228162    2.035371
           urbandum |   12.60645   16.88394     1.89   0.058     .9131075    174.0459
        deathtile10 |   1.328048   .2010269     1.87   0.061     .9871062     1.78675
    urbxdeathtile10 |   .5793285   .1006433    -3.14   0.002     .4121365    .8143455
     demonstrations |   6.710806   3.687124     3.46   0.001     2.286091    19.69953
      newpolitymin1 |   .9015132   .0327494    -2.85   0.004       .83954    .9680611
    newpolitymin1sq |   .9782702    .007012    -3.07   0.002     .9646146    .9921191
  newincumbpowerdur |   1.035659   .0208414     1.74   0.082     .9956042    1.077326
        newgdppcthl |   .7499697   .0661697    -3.26   0.001     .6308648    .8915611
          newlnoill |   .8724513   .0375008    -3.17   0.002     .8019514    .9491488
newmilexpsold10tile |   1.281502   .1008954     3.15   0.002     1.097975    1.495704
              _cons |   .0002964   .0004595    -5.24   0.000     .0000142    .0061916
-------------------------------------------------------------------------------------

. mi predict xbsuccmi using miest if startyear>1899, xb

. roctab success xbsuccmi

                      ROC                    -Asymptotic Normal--
           Obs       Area     Std. Err.      [95% Conf. Interval]
     ------------------------------------------------------------
           288     0.8837       0.0199        0.84475     0.92267

. drop xbsuccmi

. 
. log close
      name:  <unnamed>
       log:  C:\Users\mbeissin\Desktop\Stata files for book\Logfiles\chapter4.log
  log type:  text
 closed on:  25 Jan 2022, 22:09:17
------------------------------------------------------------------------------------------------------------------
